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Feature Extraction by Multi-Scale Principal Component Analysis and Classification in Spectral Domain 被引量:2
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作者 Shengkun Xie Anna T. Lawnizak +1 位作者 Pietro Lio Sridhar Krishnan 《Engineering(科研)》 2013年第10期268-271,共4页
Feature extraction of signals plays an important role in classification problems because of data dimension reduction property and potential improvement of a classification accuracy rate. Principal component analysis (... Feature extraction of signals plays an important role in classification problems because of data dimension reduction property and potential improvement of a classification accuracy rate. Principal component analysis (PCA), wavelets transform or Fourier transform methods are often used for feature extraction. In this paper, we propose a multi-scale PCA, which combines discrete wavelet transform, and PCA for feature extraction of signals in both the spatial and temporal domains. Our study shows that the multi-scale PCA combined with the proposed new classification methods leads to high classification accuracy for the considered signals. 展开更多
关键词 MULTI-SCALE principal component analysis Discrete WAVELET TRANSFORM feature Extraction Signal CLASSIFICATION Empirical CLASSIFICATION
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Feature Extraction of Fabric Defects Based on Complex Contourlet Transform and Principal Component Analysis 被引量:1
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作者 吴一全 万红 叶志龙 《Journal of Donghua University(English Edition)》 EI CAS 2013年第4期282-286,共5页
To extract features of fabric defects effectively and reduce dimension of feature space,a feature extraction method of fabric defects based on complex contourlet transform (CCT) and principal component analysis (PC... To extract features of fabric defects effectively and reduce dimension of feature space,a feature extraction method of fabric defects based on complex contourlet transform (CCT) and principal component analysis (PCA) is proposed.Firstly,training samples of fabric defect images are decomposed by CCT.Secondly,PCA is applied in the obtained low-frequency component and part of highfrequency components to get a lower dimensional feature space.Finally,components of testing samples obtained by CCT are projected onto the feature space where different types of fabric defects are distinguished by the minimum Euclidean distance method.A large number of experimental results show that,compared with PCA,the method combining wavdet low-frequency component with PCA (WLPCA),the method combining contourlet transform with PCA (CPCA),and the method combining wavelet low-frequency and highfrequency components with PCA (WPCA),the proposed method can extract features of common fabric defect types effectively.The recognition rate is greatly improved while the dimension is reduced. 展开更多
关键词 fabric defects feature extraction complex contourlet transform(CCT) principal component analysis(PCA)CLC number:TP391.4 TS103.7Document code:AArticle ID:1672-5220(2013)04-0282-05
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The Formation Mechanism of Hydrogeochemical Features in a Karst System During Storm Events as Revealed by Principal Component Analysis
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作者 Pingheng Yang Daoxian Yuan Kuang Yinglun,Wenhao Yuan,Peng Jia,Qiufang He 1.School of Geographical Sciences,Southwest University,Chongqing 400715,China. 2.Laboratory of Geochemistry and Isotope,Southwest University,Chongqing 400715,China 3.The Karst Dynamics Laboratory,Ministry of Land and Resources,Institute of Karst Geology,Chinese Academy of Geological Sciences,Guilin 541004,China 《地学前缘》 EI CAS CSCD 北大核心 2009年第S1期33-34,共2页
The hydrogeochemical parameters of Jiangjia Spring,the outlet of Qingrnuguan underground river system(QURS) in Chongqing,were found responding rapidly to storm events in late April,2008.A total of 20 kinds of hydrogeo... The hydrogeochemical parameters of Jiangjia Spring,the outlet of Qingrnuguan underground river system(QURS) in Chongqing,were found responding rapidly to storm events in late April,2008.A total of 20 kinds of hydrogeochemical parameters,including discharge,specific conductance,pH,water tempera- 展开更多
关键词 RAINFALL principal component analysis(PCA) soil EROSION AGRICULTURAL activities KARST hydrogeochemical feature Qingmuguan
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Feature Extraction of Sectorial Scan Image of Thick-Walled Electron Beam Welding Seam Based on Principal Component Analysis
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作者 Tie Gang Yilin Luan Chi Zhang 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2017年第6期45-51,共7页
A feature extraction method was proposed to sectorial scan image of Ti-6Al-4V electron beam welding seam based on principal component analysis to solve problem of high-dimensional data resulting in timeconsuming in de... A feature extraction method was proposed to sectorial scan image of Ti-6Al-4V electron beam welding seam based on principal component analysis to solve problem of high-dimensional data resulting in timeconsuming in defect recognition. Seven features were extracted from the image and represented 87. 3% information of the original data. Both the extracted features and the original data were used to train support vector machine model to assess the feature extraction performance in two aspects: recognition accuracy and training time. The results show that using the extracted features the recognition accuracy of pore,crack,lack of fusion and lack of penetration are 93%,90.7%,94.7% and 89.3%,respectively,which is slightly higher than those using the original data. The training time of the models using the extracted features is extremely reduced comparing with those using the original data. 展开更多
关键词 electron beam welding phased array ultrasonic sectorial scan image feature extraction principal component analysis
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Principal Component Feature for ANN-Based Speech Recognition
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作者 顾明亮 王太君 +1 位作者 史笑兴 何振亚 《Journal of Southeast University(English Edition)》 EI CAS 1998年第2期13-18,共6页
Using function approximation technology and principal component analysis method, this paper presents a principal component feature to solve the time alignment problem and to simplify the structure of neural network. I... Using function approximation technology and principal component analysis method, this paper presents a principal component feature to solve the time alignment problem and to simplify the structure of neural network. Its extraction simulates the processing of speech information in human auditory system. The experimental results show that the principal component feature based recognition system outperforms the standard CDHMM and GMDS method in many aspects. 展开更多
关键词 principal component analysis feature extraction speech recognition
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Support vector classifier based on principal component analysis 被引量:1
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作者 Zheng Chunhong Jiao Licheng Li Yongzhao 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2008年第1期184-190,共7页
Support vector classifier (SVC) has the superior advantages for small sample learning problems with high dimensions, with especially better generalization ability. However there is some redundancy among the high dim... Support vector classifier (SVC) has the superior advantages for small sample learning problems with high dimensions, with especially better generalization ability. However there is some redundancy among the high dimensions of the original samples and the main features of the samples may be picked up first to improve the performance of SVC. A principal component analysis (PCA) is employed to reduce the feature dimensions of the original samples and the pre-selected main features efficiently, and an SVC is constructed in the selected feature space to improve the learning speed and identification rate of SVC. Furthermore, a heuristic genetic algorithm-based automatic model selection is proposed to determine the hyperparameters of SVC to evaluate the performance of the learning machines. Experiments performed on the Heart and Adult benchmark data sets demonstrate that the proposed PCA-based SVC not only reduces the test time drastically, but also improves the identify rates effectively. 展开更多
关键词 support vector classifier principal component analysis feature selection genetic algorithms
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Polarimetric Meteorological Satellite Data Processing Software Classification Based on Principal Component Analysis and Improved K-Means Algorithm 被引量:1
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作者 Manyun Lin Xiangang Zhao +3 位作者 Cunqun Fan Lizi Xie Lan Wei Peng Guo 《Journal of Geoscience and Environment Protection》 2017年第7期39-48,共10页
With the increasing variety of application software of meteorological satellite ground system, how to provide reasonable hardware resources and improve the efficiency of software is paid more and more attention. In th... With the increasing variety of application software of meteorological satellite ground system, how to provide reasonable hardware resources and improve the efficiency of software is paid more and more attention. In this paper, a set of software classification method based on software operating characteristics is proposed. The method uses software run-time resource consumption to describe the software running characteristics. Firstly, principal component analysis (PCA) is used to reduce the dimension of software running feature data and to interpret software characteristic information. Then the modified K-means algorithm was used to classify the meteorological data processing software. Finally, it combined with the results of principal component analysis to explain the significance of various types of integrated software operating characteristics. And it is used as the basis for optimizing the allocation of software hardware resources and improving the efficiency of software operation. 展开更多
关键词 principal component analysis Improved K-Mean ALGORITHM METEOROLOGICAL Data Processing feature analysis SIMILARITY ALGORITHM
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Application of Particle Swarm Optimization to Fault Condition Recognition Based on Kernel Principal Component Analysis 被引量:1
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作者 WEI Xiu-ye PAN Hong-xia HUANG Jin-ying WANG Fu-jie 《International Journal of Plant Engineering and Management》 2009年第3期129-135,共7页
Panicle swarm optimization (PSO) is an optimization algorithm based on the swarm intelligent principle. In this paper the modified PSO is applied to a kernel principal component analysis ( KPCA ) for an optimal ke... Panicle swarm optimization (PSO) is an optimization algorithm based on the swarm intelligent principle. In this paper the modified PSO is applied to a kernel principal component analysis ( KPCA ) for an optimal kernel function parameter. We first comprehensively considered within-class scatter and between-class scatter of the sample features. Then, the fitness function of an optimized kernel function parameter is constructed, and the particle swarm optimization algorithm with adaptive acceleration (CPSO) is applied to optimizing it. It is used for gearbox condi- tion recognition, and the result is compared with the recognized results based on principal component analysis (PCA). The results show that KPCA optimized by CPSO can effectively recognize fault conditions of the gearbox by reducing bind set-up of the kernel function parameter, and its results of fault recognition outperform those of PCA. We draw the conclusion that KPCA based on CPSO has an advantage in nonlinear feature extraction of mechanical failure, and is helpful for fault condition recognition of complicated machines. 展开更多
关键词 particle swarm optimization kernel principal component analysis kernel function parameter feature extraction gearbox condition recognition
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Android Malware Detection Using Local Binary Pattern and Principal Component Analysis
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作者 Qixin Wu Zheng Qin +3 位作者 Jinxin Zhang Hui Yin Guangyi Yang Kuangsheng Hu 《国际计算机前沿大会会议论文集》 2017年第1期63-66,共4页
Nowadays,analysis methods based on big data have been widely used in malicious software detection.Since Android has become the dominator of smartphone operating system market,the number of Android malicious applicatio... Nowadays,analysis methods based on big data have been widely used in malicious software detection.Since Android has become the dominator of smartphone operating system market,the number of Android malicious applications are increasing rapidly as well,which attracts attention of malware attackers and researchers alike.Due to the endless evolution of the malware,it is critical to apply the analysis methods based on machine learning to detect malwares and stop them from leakaging our privacy information.In this paper,we propose a novel Android malware detection method based on binary texture feature recognition by Local Binary Pattern and Principal Component Analysis,which can visualize malware and detect malware accurately.Also,our method analyzes malware binary directly without any decompiler,sandbox or virtual machines,which avoid time and resource consumption caused by decompiler or monitor in this process.Experimentation on 5127 benigns and 5560 malwares shows that we obtain a detection accuracy of 90%. 展开更多
关键词 ANDROID MALWARE detection BINARY TEXTURE feature Local BINARY PATTERN principal component analysis
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Optimized ANN for LiFePO_(4) battery charge estimation using principal components based feature generation 被引量:1
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作者 Chaitali Mehta Amit V.Sant Paawan Sharma 《Green Energy and Intelligent Transportation》 2024年第4期59-71,共13页
Electric vehicles(EVs)have gained prominence in the present energy transition scenario.Widespread adoption of EVs necessitates an accurate State of Charge estimation(SoC)algorithm.Integrating predictive SoC estimation... Electric vehicles(EVs)have gained prominence in the present energy transition scenario.Widespread adoption of EVs necessitates an accurate State of Charge estimation(SoC)algorithm.Integrating predictive SoC estimations with smart charging strategies not only optimizes charging efficiency and grid reliability but also extends battery lifespan while continuously enhancing the accuracy of SoC predictions,marking a crucial milestone in sustainable electric vehicle technology.In this research study,machine learning methods,particularly Artificial Neural Networks(ANN),are employed for SoC estimation of LiFePO4 batteries,resulting in efficient and accurate estimation algorithms.The investigation first focuses on developing a custom-designed battery pack with 12V,4 Ah capacity with a facility for real-time data collection through a dedicated hardware setup.The voltage,current and open-circuit voltage of the battery are monitored with computerized battery analyzer.The battery temperature is sensed with a DHT22 temperature sensor interfaced with Raspberry Pi.Principal components are derived for the collected battery data set and analyzed for feature engineering.Three principal components were generated as input parameters for the developed ANN.Early Stopping for the ANN was also implemented to achieve faster convergence of the ANN.While considering eleven combinations for ten different optimizers loss function is minimized.Comparative analysis of hyperparameter tuning and optimizer selection revealed that the Adafactor optimizer with specific settings produced the best results with an RMSE value of 0.4083 and an R2 Score of 0.9998.The proposed algorithm was also implemented for two different types of datasets,a UDDS drive cycle and a standard cell-level dataset.The results obtained were in line with the results obtained with the ANN model developed based on the data collected from the developed experimental setup. 展开更多
关键词 BATTERY feature engineering principal component analysis Artificial neural networks Optimizers
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Comparison of debris flow susceptibility assessment methods:support vector machine,particle swarm optimization,and feature selection techniques
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作者 ZHAO Haijun WEI Aihua +3 位作者 MA Fengshan DAI Fenggang JIANG Yongbing LI Hui 《Journal of Mountain Science》 SCIE CSCD 2024年第2期397-412,共16页
The selection of important factors in machine learning-based susceptibility assessments is crucial to obtain reliable susceptibility results.In this study,metaheuristic optimization and feature selection techniques we... The selection of important factors in machine learning-based susceptibility assessments is crucial to obtain reliable susceptibility results.In this study,metaheuristic optimization and feature selection techniques were applied to identify the most important input parameters for mapping debris flow susceptibility in the southern mountain area of Chengde City in Hebei Province,China,by using machine learning algorithms.In total,133 historical debris flow records and 16 related factors were selected.The support vector machine(SVM)was first used as the base classifier,and then a hybrid model was introduced by a two-step process.First,the particle swarm optimization(PSO)algorithm was employed to select the SVM model hyperparameters.Second,two feature selection algorithms,namely principal component analysis(PCA)and PSO,were integrated into the PSO-based SVM model,which generated the PCA-PSO-SVM and FS-PSO-SVM models,respectively.Three statistical metrics(accuracy,recall,and specificity)and the area under the receiver operating characteristic curve(AUC)were employed to evaluate and validate the performance of the models.The results indicated that the feature selection-based models exhibited the best performance,followed by the PSO-based SVM and SVM models.Moreover,the performance of the FS-PSO-SVM model was better than that of the PCA-PSO-SVM model,showing the highest AUC,accuracy,recall,and specificity values in both the training and testing processes.It was found that the selection of optimal features is crucial to improving the reliability of debris flow susceptibility assessment results.Moreover,the PSO algorithm was found to be not only an effective tool for hyperparameter optimization,but also a useful feature selection algorithm to improve prediction accuracies of debris flow susceptibility by using machine learning algorithms.The high and very high debris flow susceptibility zone appropriately covers 38.01%of the study area,where debris flow may occur under intensive human activities and heavy rainfall events. 展开更多
关键词 Chengde feature selection Support vector machine Particle swarm optimization principal component analysis Debris flow susceptibility
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Feature evaluation and extraction based on neural network in analog circuit fault diagnosis 被引量:16
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作者 Yuan Haiying Chen Guangju Xie Yongle 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2007年第2期434-437,共4页
Choosing the right characteristic parameter is the key to fault diagnosis in analog circuit. The feature evaluation and extraction methods based on neural network are presented. Parameter evaluation of circuit feature... Choosing the right characteristic parameter is the key to fault diagnosis in analog circuit. The feature evaluation and extraction methods based on neural network are presented. Parameter evaluation of circuit features is realized by training results from neural network; the superior nonlinear mapping capability is competent for extracting fault features which are normalized and compressed subsequently. The complex classification problem on fault pattern recognition in analog circuit is transferred into feature processing stage by feature extraction based on neural network effectively, which improves the diagnosis efficiency. A fault diagnosis illustration validated this method. 展开更多
关键词 Fault diagnosis feature extraction Analog circuit Neural network principal component analysis.
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Adaptive WNN aerodynamic modeling based on subset KPCA feature extraction 被引量:4
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作者 孟月波 邹建华 +1 位作者 甘旭升 刘光辉 《Journal of Central South University》 SCIE EI CAS 2013年第4期931-941,共11页
In order to accurately describe the dynamic characteristics of flight vehicles through aerodynamic modeling, an adaptive wavelet neural network (AWNN) aerodynamic modeling method is proposed, based on subset kernel pr... In order to accurately describe the dynamic characteristics of flight vehicles through aerodynamic modeling, an adaptive wavelet neural network (AWNN) aerodynamic modeling method is proposed, based on subset kernel principal components analysis (SKPCA) feature extraction. Firstly, by fuzzy C-means clustering, some samples are selected from the training sample set to constitute a sample subset. Then, the obtained samples subset is used to execute SKPCA for extracting basic features of the training samples. Finally, using the extracted basic features, the AWNN aerodynamic model is established. The experimental results show that, in 50 times repetitive modeling, the modeling ability of the method proposed is better than that of other six methods. It only needs about half the modeling time of KPCA-AWNN under a close prediction accuracy, and can easily determine the model parameters. This enables it to be effective and feasible to construct the aerodynamic modeling for flight vehicles. 展开更多
关键词 WAVELET neural network fuzzy C-means clustering kernel principal components analysis feature extraction aerodynamic modeling
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Finger crease pattern recognition using Legendre moments and principal component analysis 被引量:2
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作者 罗荣芳 林土胜 《Chinese Optics Letters》 SCIE EI CAS CSCD 2007年第3期160-163,共4页
The finger joint lines defined as finger creases and its distribution can identify a person. In this paper, we propose a new finger crease pattern recognition method based on Legendre moments and principal component a... The finger joint lines defined as finger creases and its distribution can identify a person. In this paper, we propose a new finger crease pattern recognition method based on Legendre moments and principal component analysis (PCA). After obtaining the region of interest (ROI) for each finger image in the pre- processing stage, Legendre moments under Radon transform are applied to construct a moment feature matrix from the ROI, which greatly decreases the dimensionality of ROI and can represent principal components of the finger creases quite well. Then, an approach to finger crease pattern recognition is designed based on Karhunen-Loeve (K-L) transform. The method applies PCA to a moment feature matrix rather than the original image matrix to achieve the feature vector. The proposed method has been tested on a database of 824 images from 103 individuals using the nearest neighbor classifier. The accuracy up to 98.584% has been obtained when using 4 samples per class for training. The experimental results demonstrate that our proposed approach is feasible and effective in biometrics. 展开更多
关键词 BIOMETRICS Database systems feature extraction Mathematical transformations Pattern recognition principal component analysis
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Spectral Matching Classification Method of Multi-State Similar Pigments Based on Feature Differences 被引量:2
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作者 Meng Da Huiqin Wang +1 位作者 Ke Wang Zhan Wang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2022年第4期513-527,共15页
The properties of the same pigments in murals are affected by different concentrations and particle diameters,which cause the shape of the spectral reflectance data curve to vary,thus influencing the outcome of matchi... The properties of the same pigments in murals are affected by different concentrations and particle diameters,which cause the shape of the spectral reflectance data curve to vary,thus influencing the outcome of matching calculations.This paper proposes a spectral matching classification method of multi-state similar pigments based on feature differences.Fast principal component analysis(FPCA)was used to calculate the eigenvalue variance of pigment spectral reflectance,then applied to the original reflectance values for parameter characterization.We first projected the original spectral reflectance from the spectral space to the characteristic variance space to identify the spectral curve.Secondly,the relative distance between the eigenvalues in the eigen variance space is combined with the JS(Jensen-Shannon)divergence to express the difference between the two spectral distributions.The JS information divergence calculates the relative distance between the eigenvalues.Experimental results showthat our classification method can be used to identify the spectral curves of the same pigment under different states.The value of the root means square error(RMSE)decreased by 12.0817,while the mean values of the mean absolute percentage error(MAPE)and R2 increased by 0.0965 and 0.2849,respectively.Compared with the traditional spectral matching algorithm,the recognition error was effectively reduced. 展开更多
关键词 Fast principal component analysis paint samples REFLECTIVITY information divergence feature variance
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Chemometric Feature Selection and Classification of <i>Ganoderma lucidum</i>Spores and Fruiting Body Using ATR-FTIR Spectroscopy 被引量:2
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作者 Ying Zhu Augustine Tuck Lee Tan 《American Journal of Analytical Chemistry》 2015年第10期830-840,共11页
Ganoderma lucidum(G. lucidum) spores as a valuable Chinese herbal medicine have vast marketable prospect for its bioactivities and medicinal efficacy. This study aims at the development of an effective and simple anal... Ganoderma lucidum(G. lucidum) spores as a valuable Chinese herbal medicine have vast marketable prospect for its bioactivities and medicinal efficacy. This study aims at the development of an effective and simple analytical method to distinguish G. lucidum spores from its fruiting body, which is of essential importance for the quality control and fast discrimination of raw materials of Chinese herbal medicine. Attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy combined with the appropriate chemometric methods including penalized discriminant analysis, principal component discriminant analysis and partial least squares discriminant analysis has been proven to be a rapid and powerful tool for discrimination of G. lucidum spores and its fruiting body with classification accuracy of 99%. The model leads to a well-performed selection of informative spectral absorption bands which improve the classification accuracy, reduce the model complexity and enhance the quantitative interpretations of the chemical constituents of G. lucidum spores regarding its anticancer effects. 展开更多
关键词 feature Selection Attenuated Total Reflection Fourier Transform Infrared Spectroscopy Penalized Linear DISCRIMINANT analysis principal component DISCRIMINANT analysis Partial Least Squares DISCRIMINANT analysis
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Feasibility Study of the GST‑SVD in Extracting the Fault Feature of Rolling Bearing under Variable Conditions 被引量:1
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作者 Xiangnan Liu Xuezhi Zhao Kuanfang He 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2022年第6期326-339,共14页
Feature information extraction is one of the key steps in prognostics and health management of rotating machinery.In the present study,an investigation about the feasibility of a methodology based on generalized S tra... Feature information extraction is one of the key steps in prognostics and health management of rotating machinery.In the present study,an investigation about the feasibility of a methodology based on generalized S transform(GST)and singular value decomposition(SVD)methods for feature extraction in rolling bearing,due to local damage under variable conditions,is conducted.The technique adopts the GST method,following the time-frequency analysis,to transform a raw fault signal of the rolling bearing into a two-dimensional complex matrix.And then,the SVD method is performed to decompose the matrix to obtain the feature vectors.By this procedure it is possible to obtain the fault feature information of rolling bearing under different speeds and different loads.In order to streamline the feature parameters of the feature vectors to train more uncomplicated models,the principal component analysis(PCA)subsequently performed.The particle swarm optimization-support vector machine(PSO-SVM)model is used to identify and classify the different fault states of rolling bearing.Furthermore,in order to highlight the superiority of the proposed method some comparisons are conducted with the conventional methods.The obtained results show that the proposed method can effectively extract fault features of the rolling bearing under variable conditions. 展开更多
关键词 feature extraction Generalized Stockwell transform Singular value decomposition principal component analysis
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Chi-Square and PCA Based Feature Selection for Diabetes Detection with Ensemble Classifier 被引量:1
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作者 Vaibhav Rupapara Furqan Rustam +2 位作者 Abid Ishaq Ernesto Lee Imran Ashraf 《Intelligent Automation & Soft Computing》 SCIE 2023年第5期1931-1949,共19页
Diabetes mellitus is a metabolic disease that is ranked among the top 10 causes of death by the world health organization.During the last few years,an alarming increase is observed worldwide with a 70%rise in the dise... Diabetes mellitus is a metabolic disease that is ranked among the top 10 causes of death by the world health organization.During the last few years,an alarming increase is observed worldwide with a 70%rise in the disease since 2000 and an 80%rise in male deaths.If untreated,it results in complications of many vital organs of the human body which may lead to fatality.Early detection of diabetes is a task of significant importance to start timely treatment.This study introduces a methodology for the classification of diabetic and normal people using an ensemble machine learning model and feature fusion of Chi-square and principal component analysis.An ensemble model,logistic tree classifier(LTC),is proposed which incorporates logistic regression and extra tree classifier through a soft voting mechanism.Experiments are also performed using several well-known machine learning algorithms to analyze their performance including logistic regression,extra tree classifier,AdaBoost,Gaussian naive Bayes,decision tree,random forest,and k nearest neighbor.In addition,several experiments are carried out using principal component analysis(PCA)and Chi-square(Chi-2)fea-tures to analyze the influence of feature selection on the performance of machine learning classifiers.Results indicate that Chi-2 features show high performance than both PCA features and original features.However,the highest accuracy is obtained when the proposed ensemble model LTC is used with the proposed fea-ture fusion framework-work which achieves a 0.85 accuracy score which is the highest of the available approaches for diabetes prediction.In addition,the statis-tical T-test proves the statistical significance of the proposed approach over other approaches. 展开更多
关键词 Diabetes mellitus prediction feature fusion ensemble classifier principal component analysis CHI-SQUARE
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Multi-modal face parts fusion based on Gabor feature for face recognition 被引量:1
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作者 相燕 《High Technology Letters》 EI CAS 2009年第1期70-74,共5页
A novel face recognition method, which is a fusion of muhi-modal face parts based on Gabor feature (MMP-GF), is proposed in this paper. Firstly, the bare face image detached from the normalized image was convolved w... A novel face recognition method, which is a fusion of muhi-modal face parts based on Gabor feature (MMP-GF), is proposed in this paper. Firstly, the bare face image detached from the normalized image was convolved with a family of Gabor kernels, and then according to the face structure and the key-points locations, the calculated Gabor images were divided into five parts: Gabor face, Gabor eyebrow, Gabor eye, Gabor nose and Gabor mouth. After that multi-modal Gabor features were spatially partitioned into non-overlapping regions and the averages of regions were concatenated to be a low dimension feature vector, whose dimension was further reduced by principal component analysis (PCA). In the decision level fusion, match results respectively calculated based on the five parts were combined according to linear discriminant analysis (LDA) and a normalized matching algorithm was used to improve the performance. Experiments on FERET database show that the proposed MMP-GF method achieves good robustness to the expression and age variations. 展开更多
关键词 Gabor filter multi-modal Gabor features principal component analysis (PCA) linear discriminant analysis (IDA) normalized matching algorithm
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Image Retrieval Based on Deep Feature Extraction and Reduction with Improved CNN and PCA 被引量:2
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作者 Rongyu Chen Lili Pan +1 位作者 Yan Zhou Qianhui Lei 《Journal of Information Hiding and Privacy Protection》 2020年第2期67-76,共10页
With the rapid development of information technology,the speed and efficiency of image retrieval are increasingly required in many fields,and a compelling image retrieval method is critical for the development of info... With the rapid development of information technology,the speed and efficiency of image retrieval are increasingly required in many fields,and a compelling image retrieval method is critical for the development of information.Feature extraction based on deep learning has become dominant in image retrieval due to their discrimination more complete,information more complementary and higher precision.However,the high-dimension deep features extracted by CNNs(convolutional neural networks)limits the retrieval efficiency and makes it difficult to satisfy the requirements of existing image retrieval.To solving this problem,the high-dimension feature reduction technology is proposed with improved CNN and PCA quadratic dimensionality reduction.Firstly,in the last layer of the classical networks,this study makes a well-designed DR-Module(dimensionality reduction module)to compress the number of channels of the feature map as much as possible,and ensures the amount of information.Secondly,the deep features are compressed again with PCA(Principal Components Analysis),and the compression ratios of the two dimensionality reductions are reduced,respectively.Therefore,the retrieval efficiency is dramatically improved.Finally,it is proved on the Cifar100 and Caltech101 datasets that the novel method not only improves the retrieval accuracy but also enhances the retrieval efficiency.Experimental results strongly demonstrate that the proposed method performs well in small and medium-sized datasets. 展开更多
关键词 Image retrieval deep features convolutional neural networks principal components analysis
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