Modified algorithm of principal component analysis for face recognition

In principal component analysis （PCA） algorithms for face recognition, to reduce the influence of the eigenvectors which relate to the changes of the illumination on abstract features, a modified PCA （MPCA） algorithm is proposed. The method is based on the idea of reducing the influence of the eigenvectors associated with the large eigenvalues by normalizing the feature vector element by its corresponding standard deviation. The Yale face database and Yale face database B are used to verify the method. The simulation results show that, for front face and even under the condition of limited variation in the facial poses, the proposed method results in better performance than the conventional PCA and linear discriminant analysis （LDA） approaches, and the computational cost remains the same as that of the PCA, and much less than that of the LDA.

On-line Batch Process Monitoring and Diagnosing Based on Fisher Discriminant Analysis

A new on-line batch process monitoring and diagnosing approach based on Fisher discriminant analysis （FDA） was proposed. This method does not need to predict the future observations of variables, so it is more sensitive to fault detection and stronger implement for monitoring. In order to improve the monitoring performance, the variables trajectories of batch process are separated into several blocks. The key to the proposed approach for on-line monitoring is to calculate the distance of block data that project to low-dimension Fisher space between new batch and reference batch. Comparing the distance with the predefine threshold, it can be considered whether the batch process is normal or abnormal. Fault diagnosis is performed based on the weights in fault direction calculated by FDA. The proposed method was applied to the simulation model of fed-batch penicillin fermentation and the resuits were compared with those obtained using MPCA. The simulation results clearly show that the on-line monitoring method based on FDA is more efficient than the MPCA.

Principal Component-Discrimination Model and Its Application

Having researched for many years, seismologists in China presented about 80 earthquake prediction factors which reflected omen information of earthquake. How to concentrate the information that the 80 earthquake prediction factors have and how to choose the main factors to predict earthquakes precisely have become one of the topics in seismology. The model of principal component-discrimination consists of principal component analysis, correlation analysis, weighted method of principal factor coefficients and Mahalanobis distance discrimination analysis. This model combines the method of maximization earthquake prediction factor information with the weighted method of principal factor coefficients and correlation analysis to choose earthquake prediction variables, applying Mahalanobis distance discrimination to establishing earthquake prediction discrimination model. This model was applied to analyzing the earthquake data of Northern China area and obtained good prediction results.

Near-Infrared Spectroscopy Combined with Partial Least Squares Discriminant Analysis Applied to Identification of Liquor Brands

The identification of liquor brands is very important for food safety. Most of the fake liquors are usually made into the products with the same flavor and alcohol content as regular brand, so the identification for the liquor brands with the same flavor and the same alcohol content is essential. However, it is also difficult because the components of such liquor samples are very similar. Near-infrared (NIR) spectroscopy combined with partial least squares discriminant analysis (PLS-DA) was applied to identification of liquor brands with the same flavor and alcohol content. A total of 160 samples of Luzhou Laojiao liquor and 200 samples of non-Luzhou Laojiao liquor with the same flavor and alcohol content were used for identification. Samples of each type were randomly divided into the modeling and validation sets. The modeling samples were further divided into calibration and prediction sets using the Kennard-Stone algorithm to achieve uniformity and representativeness. In the modeling and validation processes based on PLS-DA method, the recognition rates of samples achieved 99.1% and 98.7%, respectively. The results show high prediction performance for the identification of liquor brands, and were obviously better than those obtained from the principal component linear discriminant analysis method. NIR spectroscopy combined with the PLS-DA method provides a quick and effective means of the discriminant analysis of liquor brands, and is also a promising tool for large-scale inspection of liquor food safety.

Moving-window bis-correlation coefficients method for visible and near-infrared spectral discriminant analysis with applications

The moving window bis corelation coefficients(MW BiCC)was proposed and employed for the discriminant analysis of transgenic sugarcane leaves and B-thalassemia with visible and near-infrared(Vis NIR)spectroscopy.The well-performed moving window principal component analysis linear discriminant analysis(MWPCA-LDA)was also conducted for comparison.A total of 306 transgenic(positive)and 150 nont ransgenic(negative)leave samples of sugarcane were collected and divided to calibration,prediction,and validation.The diffuse reflection spectra were corected using Savitzky-Golay(SG)smoothing with first-order derivative(d=1),third-degree polynomial(p=3)and 25 smpothing points(m=25).The selected waveband was 736-1054nm with MW-BiCC,and the positive and negative validation recognition rates(V_REC^(+),VREC^(-))were 100%,98.0%,which achieved the same effect as MWPCA-LDA.Another example,the 93 B-thalassemia(positive)and 148 nonthalassemia(negative)of human hemolytic samples were colloctod.The transmission spectra were corrected using SG smoothing withd=1,p=3 and m=53.Using M W-BiCC,many best wavebands were selected(e.g.,1116-1146,17941848 and 22842342nm).The V_REC^(+)and V_REC^(-)were both 100%,which achieved the same effect as MW-PCA-LDA.Importantly,the BICC only required ca lculating correlation cofficients between the spectrum of prediction sample and the average spectra of two types of calibration samples.Thus,BiCC was very simple in algorithm,and expected to obtain more applications.The results first confirmed the feasibility of distinguishing B-thalassemia and normal control samples by NIR spectroscopy,and provided a promising simple tool for large population thalassemia screening.

Multispectral Imaging in Combination with Multivariate Analysis Discriminates Selenite Induced Cataractous Lenses from Healthy Lenses of Sprague-Dawley Rats

Cataracts are the leading cause of blindness worldwide. Current methods for discriminating cataractous lenses from healthy lenses of Sprague-Dawley rats during preclinical studies are based on either histopathological or clinical assessments which are weakened by subjectivity. In this work, both cataractous and healthy lens tissues of Sprague-Dawley rats were studied using multispectral imaging technique in combination with multivariate analysis. Multispectral images were captured in transmission, reflection and scattering modes. In all, five spectral bands were found to be markers for discriminating cataractous lenses from healthy lenses;470 nm and 625 nm discriminated in reflection mode whereas 435 nm, 590 nm and 700 nm discriminated in transmission mode. With Fisher’s Linear discriminant analysis, the midpoints for classifying cataractous from healthy lenses were found to be 14.718 × 10−14 and 3.2374 × 10−14 for the two spectra bands in the reflection mode and the three spectral bands in the transmission mode respectively. Images in scattering mode did not show significant discrimination. These spectral bands in reflection and transmission modes may offer potential diagnostic markers for discriminating cataractous lenses from healthy lenses thereby promising multispectral imaging applications for characterizing cataractous and healthy lenses.

Discrimination of toxic ingredient between raw and processed Pinellia ternata by UPLC/Q-TOF-MS/MS with principal component analysis and T-test

Objective: To investigate the toxicity difference between raw and processed Pinelliae Rhizoma(Banxia in Chinese, BX), the rhizoma of Pinellia ternata, from the view of chemical composition.Methods: Sixteen samples of raw and processed BX were prepared and analyzed by UPLC/Q-TOF-MS/MS.The discrimination(chemical marker) between the two group was investigated by principal component analysis(PCA) and T-test analysis. According to the accurate charge-to-mass ratio, MS/MS fragments, and comparison of corresponding data with the reference or database, the chemical markers were identified preliminarily.Results: Liquiritin, liquiritigenin, and lysophosphatidylcholine(LPC) were identified as the characteristic markers. The reducing of LPC in processed BX was one of the main reasons for detoxification because LPC could induce the inflammatory response;Liquiritin and liquiritigenin showed the anti-inflammatory effect and reduced liver injury, therefore the appearance of them in processed BX was an another reason for detoxification.Conclusion: An approach to explain the mechanisms of reducing the toxicity in medicinal plants by processing was proposed. Moreover, the chemical markers of toxicity could be used to differentiate the raw material from processed herbs for the quality control and safety application in clinical practice.

Metabolome Comparison of Transgenic and Non-transgenic Rice by Statistical Analysis of FTIR and NMR Spectra

Modern biotechnology, based on recombinant DNA techniques, has made it possible to introduce new traits with great potential for crop improvement. However, concerns about unintended effects of gene transformation that possibly threaten environment or consumer health have persuaded scientists to set up pre-release tests on genetically modified organisms. Assessment of ＇substantial equivalence＇ concept that established by comparison of genetically modified organism with a comparator with a history of safe use could be the first step of a comprehensive risk assessment. Metabolite level is the dchest in performance of changes which stem from genetic or environmental factors. Since assessment of all metabolites in detail is very costly and practically impossible, statistical evaluation of processed data of grain spectroscopic values could be a time and cost effective substitution for complex chemical analysis. To investigate the ability of multivariate statistical techniques in comparison of metabolomes as well as testing a method for such comparisons with available tools, a transgenic rice in combination with its traditionally bred parent were used as test material, and the discriminant analysis were applied as supervised method and principal component analysis as unsupervised classification method on the processed data which were extracted from Fourier transform infrared spectroscopy and nuclear magnetic resonance spectral data of powdered rice and rice extraction and badey grain samples, of which the latter was considered as control. The results confirmed the capability of statistics, even with initial data processing applications in metabolome studies. Meanwhile, this study confirms that the supervised method results in more distinctive results.

Machine Learning-based USD/PKR Exchange Rate Forecasting Using Sentiment Analysis of Twitter Data

This study proposes an approach based on machine learning to forecast currency exchange rates by applying sentiment analysis to messages on Twitter(called tweets).A dataset of the exchange rates between the United States Dollar(USD)and the Pakistani Rupee(PKR)was formed by collecting information from a forex website as well as a collection of tweets from the business community in Pakistan containing finance-related words.The dataset was collected in raw form,and was subjected to natural language processing by way of data preprocessing.Response variable labeling was then applied to the standardized dataset,where the response variables were divided into two classes:“1”indicated an increase in the exchange rate and“−1”indicated a decrease in it.To better represent the dataset,we used linear discriminant analysis and principal component analysis to visualize the data in three-dimensional vector space.Clusters that were obtained using a sampling approach were then used for data optimization.Five machine learning classifiers—the simple logistic classifier,the random forest,bagging,naïve Bayes,and the support vector machine—were applied to the optimized dataset.The results show that the simple logistic classifier yielded the highest accuracy of 82.14%for the USD and the PKR exchange rates forecasting.

Prioritization of Sub-Watersheds in a Large Semi-Arid Drainage Basin (Southern Jordan) Using Morphometric Analysis, GIS, and Multivariate Statistics

GIS-based morphometric analysis was employed to prioritize the W. Mujib-Wala watershed southern Jordan. Seventy six fourth-order sub-watersheds were prioritized using morphometric analysis of ten linear and shape parameters. Each sub-watershed is prioritized by designated ranks based on the calculated compound parameter (Cp). The total score for each sub-basin is assigned as per erosion threat. The 76 sub-basins were grouped into four categories of priority: very high (12 sub-basins, 15.8% of the total), high (32 sub-watersheds, 42.1% of the total), moderate (25 sub-watersheds, 32.9% of the total), and low (7 sub-watersheds, 9.2% of the total). Sub-watersheds categorized as very high and high are subjected to high erosion risk, thus creating an urgent need for applying soil and water conservation measures. The relative diversity in land use practices and land cover, including variation in slope and soil types, are considered in proposing suitable conservation structures for sub-watersheds connected to each priority class. The adaptation of soil conservation measures priority-wise will reduce the erosivity effect on soil loss;while increasing infiltration rates;and water availability in soil profile. Principal component analysis (PCA) reduces the basic parameters and erosion risk parameters to three components, explaining 88% of the variance. The relationships of these components to the basic and erosion risk parameters were evaluated, and then the degree of inter-correlation among the morphometric parameters was explored. The verification of priority classes obtained through morphometric analysis was tested using Discriminant Analysis (DA). The results show a complete separation existing between the identified priority classes. Thus, soil erosion risk and geomorphic conditions are found entirely different from one class to another. The present results are intended to help decision makers to plan for efficient soil and water conservation measures to achieve future agricultural sustainability in the rainfed highlands of Jordan.

Image Analysis in Microbiology: A Review

This review is focused on using computer image analysis as a means of objective and quantitative characterizing optical images of the macroscopic (e.g. microbial colonies) and the microscopic (e.g. single cell) objects in the microbiological research. This is the way of making many visual inspection assays more objective and less time and labor consuming. Also, it can provide new visually inaccessible information on relation between some optical parameters and various biological features of the microbial cul-tures. Of special interest is application of image analysis in fluorescence microscopy as it opens new ways of using fluorescence based methodology for single microbial cell studies. Examples of using image analysis in the studies of both the macroscopic and the microscopic microbiological objects obtained by various imaging techniques are presented and discussed.

Functional Data Analysis of Spectroscopic Data with Application to Classification of Colon Polyps

In this study, two functional logistic regression models with functional principal component basis (FPCA) and functional partial least squares basis (FPLS) have been developed to distinguish precancerous adenomatous polyps from hyperplastic polyps for the purpose of classification and interpretation. The classification performances of the two functional models have been compared with two widely used multivariate methods, principal component discriminant analysis (PCDA) and partial least squares discriminant analysis (PLSDA). The results indicated that classification abilities of FPCA and FPLS models outperformed those of the PCDA and PLSDA models by using a small number of functional basis components. With substantial reduction in model complexity and improvement of classification accuracy, it is particularly helpful for interpretation of the complex spectral features related to precancerous colon polyps.

Functional Analysis of Chemometric Data

The objective of this paper is to present a review of different calibration and classification methods for functional data in the context of chemometric applications. In chemometric, it is usual to measure certain parameters in terms of a set of spectrometric curves that are observed in a finite set of points (functional data). Although the predictor variable is clearly functional, this problem is usually solved by using multivariate calibration techniques that consider it as a finite set of variables associated with the observed points (wavelengths or times). But these explicative variables are highly correlated and it is therefore more informative to reconstruct first the true functional form of the predictor curves. Although it has been published in several articles related to the implementation of functional data analysis techniques in chemometric, their power to solve real problems is not yet well known. Because of this the extension of multivariate calibration techniques (linear regression, principal component regression and partial least squares) and classification methods (linear discriminant analysis and logistic regression) to the functional domain and some relevant chemometric applications are reviewed in this paper.

A new image processing method for discriminating internal layers from radio echo sounding data of ice sheets via a combined robust principal component analysis and total variation approach

Discriminating internal layers by radio echo sounding is important in analyzing the thickness and ice deposits in the Antarctic ice sheet.The signal processing method of synthesis aperture radar(SAR)has been widely used for improving the signal to noise ratio(SNR)and discriminating internal layers by radio echo sounding data of ice sheets.This method is not efficient when we use edge detection operators to obtain accurate information of the layers,especially the ice-bed interface.This paper presents a new image processing method via a combined robust principal component analysis-total variation(RPCA-TV)approach for discriminating internal layers of ice sheets by radio echo sounding data.The RPCA-based method is adopted to project the high-dimensional observations to low-dimensional subspace structure to accelerate the operation of the TV-based method,which is used to discriminate the internal layers.The efficiency of the presented method has been tested on simulation data and the dataset of the Institute of Electronics,Chinese Academy of Sciences,collected during CHINARE 28.The results show that the new method is more efficient than the previous method in discriminating internal layers of ice sheets by radio echo sounding data.

基于PCA的Fisher多元统计方法识别矿井充水水源

采煤工作面回采过程中充水水源的多源不确定性是矿井水害防治的研究重点,采用水化学信息识别充水水源时,常难以明确界定各充水水源的特征型水质阈值,且现有大多数分析判别方法判别精度不高。基于现场采集的马脊梁煤矿8210工作面矿井涌水可能充水水源样本建立样本数据库,采用Piper三线图法和多因子法分析各充水水源的水质类型及训练样本数据库,建立了基于主成分分析的Fisher判别模型,并根据欧氏距离判别原则分析识别采空区涌水的充水水源。结果表明,充水水源主要为侏罗系采空积水,其次为底板灰岩水和顶板砂岩水;该判别模型判别精度可以达到99.9%,对于采煤工作面矿井涌水充水水源的现场识别具有重要指导意义。

改进Fisher判别法的突水水源快速判别模型

为快速有效地判别矿井突水水源位置,预防水害事故的发生,根据平煤五矿实际水文地质特征,针对砂岩、太灰和寒灰含水层水质差异性,收集34组水样资料,并选取K^(+)+Na^(+)、Ca^(2+)、Mg^(2+)、SO_(4)^(2-)、Cl^(-)、HCO_(3)^(-)六大常规水化学离子作为判别因子,开展水化学特征分析;结合PCA降维统计算法,建立改进的Fisher水源判别模型,并利用待测样本对比改进前后Fisher模型的判别结果,同时将训练样本回代到改进模型中进行验证。结果表明,根据水化学类型无法准确区分寒灰水与太灰水;利用改进Fisher判别模型测试10组待测样本,判别准确率为100%,相较于基础Fisher模型,准确率提高了20%,应用改进Fisher判别模型可大幅提升水源识别准确率;已知训练样本的回代结果显示,改进Fisher判别结果与实际情况基本吻合。通过2种模型的对比分析,采用改进Fisher模型进行矿井水源识别准确率及可靠性高,具有一定研究价值,可为矿井水源识别提供新的思路。

SPEECH EMOTION RECOGNITION USING MODIFIED QUADRATIC DISCRIMINATION FUNCTION

Quadratic Discrimination Function （QDF） is commonly used in speech emotion recognition, which proceeds on the premise that the input data is normal distribution. In this paper, we propose a transformation to normalize the emotional features, emotion recognition. Features based on prosody then derivate a Modified QDF （MQDF） to speech and voice quality are extracted and Principal Component Analysis Neural Network （PCANN） is used to reduce dimension of the feature vectors. The results show that voice quality features are effective supplement for recognition, and the method in this paper could improve the recognition ratio effectively.

Discrimination of Wild-Grown and Cultivated <i>Ganoderma lucidum</i>by Fourier Transform Infrared Spectroscopy and Chemometric Methods

Wild-grown Ganoderma lucidum (G. lucidum), a traditional Chinese herbal medicine, is highly cherished and expensive for its medicinal efficiency. This study targets the development of an accurate and effective analytical method to distinguish wild-grown G. lucidum from cultivated ones, which are of essential importance for the quality assurance and estimation of its medicinal value. Furthermore, different parts of G. lucidum have been studied to examine the differences between wild-grown and cultivated ones. Fourier transform infrared (FTIR) diffuse reflectance spectroscopy combined with the appropriate chemometric method has been proven to be a rapid and powerful tool for discrimination of wild-grown and cultivated G. lucidum with classification accuracy of 98%. The informative spectral absorption bands for discrimination emphasized by the linear diagnostic rule have provided quantitative interpretations of the chemical constituents of wild-grown G. lucidum regarding its anticancer effects.

Morphometric discrimination between females of two isomorphic sand fly species, Phlebotomus caucasicus and Phlebotomus mongolensis(Diptera:Phlebotominae) in endemic and non-endemic foci of zoonotic cutaneous leishmaniasis in Iran

Objective: To delineate reliable morphological characteristics for identifying and separating female Phlebotomus caucasicus and Phlebotomus mongolensis which exist sympatrically in the main foci of zoonotic cutaneous leishmaniasis in Iran.Methods: Sand flies were collected using sticky trap papers from active colonies of rodent burrows installed from 16 catching sites. Morphometric measurements were analyzed of 87 Phlebotomus caucasicus and 156 Phlebotomus mongolensis. Univariate and multivariate analysis were carried out to determine significant morphometric variables for discrimination of the two species. Finally, seven morphological characteristics of 65 female Phlebotomus caucasicus and 124 female Phlebotomus mongolensis were described.Results: Univariate and multivariate analyses of 10 morphometric variables via Discriminant Function Analysis(DFA) and Principal Component Analysis(PCA) showed that five morphometric variables had an accuracy of 100% for discriminating female Phlebotomus caucasicus and Phlebotomus mongolensis. Moreover, PCA revealed that the five morphometric variables with the highest loadings separated these two species. Morphological studies on antennal flagellum(and its associated structures) and mouth-parts of female specimens demonstrated significant differences in several structures.Conclusions: The results show that morphological and morphometrical features can be used to discriminate two female isomorphic species, Phlebotomus caucasicus and Phlebotomus mongolensis accurately.

A computer aided detection framework for mammographic images using fisher linear discriminant and nearest neighbor classifier

Today, mammography is the best method for early detection of breast cancer. Radiologists failed to detect evident cancerous signs in approximately 20% of false negative mammograms. False negatives have been identified as the inability of the radiologist to detect the abnormalities due to several reasons such as poor image quality, image noise, or eye fatigue. This paper presents a framework for a computer aided detection system that integrates Principal Component Analysis (PCA), Fisher Linear Discriminant (FLD), and Nearest Neighbor Classifier (KNN) algorithms for the detection of abnormalities in mammograms. Using normal and abnormal mammograms from the MIAS database, the integrated algorithm achieved 93.06% classification accuracy. Also in this paper, we present an analysis of the integrated algorithm’s parameters and suggest selection criteria.