Cross-Project Software Defect Prediction Based on SMOTE and Deep Canonical Correlation Analysis

Cross-Project Defect Prediction(CPDP)is a method that utilizes historical data from other source projects to train predictive models for defect prediction in the target project.However,existing CPDP methods only consider linear correlations between features(indicators)of the source and target projects.These models are not capable of evaluating non-linear correlations between features when they exist,for example,when there are differences in data distributions between the source and target projects.As a result,the performance of such CPDP models is compromised.In this paper,this paper proposes a novel CPDP method based on Synthetic Minority Oversampling Technique(SMOTE)and Deep Canonical Correlation Analysis(DCCA),referred to as S-DCCA.Canonical Correlation Analysis(CCA)is employed to address the issue of non-linear correlations between features of the source and target projects.S-DCCA extends CCA by incorporating the MlpNet model for feature extraction from the dataset.The redundant features are then eliminated by maximizing the correlated feature subset using the CCA loss function.Finally,cross-project defect prediction is achieved through the application of the SMOTE data sampling technique.Area Under Curve(AUC)and F1 scores(F1)are used as evaluation metrics.This paper conducted experiments on 27 projects from four public datasets to validate the proposed method.The results demonstrate that,on average,our method outperforms all baseline approaches by at least 1.2%in AUC and 5.5%in F1 score.This indicates that the proposed method exhibits favorable performance characteristics.

Canonical Correlation Analysis of Agronomic Characters of Brassica juncea in Western China

[Objective] The study aimed at exploring the relationship among the agronomic characters of B. juncea in western China, in order to provide scientific basis for the breeding of B. juncea in western China. [Method] 39 B. juncea materials from western China were used for the canonical correlation analysis, and canonical correlations between each pair of the four ecological character （containing 18 variables） were verified, including yield characters （5 variables）, caulis characters （6 variables）, branch characters （3 variables） and pod characters （3 variables）. [Result] Yield per plant of B. juncea in western China suffered a tremendous influence from effective pod number per plant while was not significantly affected by the total pod number per plant, seed number per pod and 1 000-seed weight; the most important character related with the yield character of B. juncea in western China was caulis character, followed by the branch character and pod character; yield characters, caulis characters, branch characters and pod characters of B. juncea in western China were closely correlated. [Conclusion] In order to improve the yield characters of B. juncea in western China, caulis characters should be focused on, followed by branch characters and pod characters; rapeseed varieties with high performance in total pod number per plant and effective pod number per plant should be chosen through the perspectives of effective branch number, plant height, pod number of main inflorescence, fruit stalk number of main inflorescence and other traits, while rapeseed varieties with high performance in seed number per pod and 1 000-seed weight should be chosen through the perspectives of beak length and other traits.

Canonical Correlation Analysis on Natural Subsystem and Economic Subsystem——Taking the Eco-development of Hebei Province as an Example

[Objective] This study aimed to provide referential basis for ecosystem study of Hebei Province in the new decade of 21st century, by investigating the correlation between natural subsystem and economic subsystem. [Method] The correlation between natural subsystem and economic subsystem in ecosystem was discussed by means of the canonical correlation, and the correlation between variables was explored with statistical data. [Result] The canonical correlation between the two subsystems could be explained by three groups of typical variables, Le., the canonical correlation between crop production and consumption; the canonical correlation between agricultural crop production, aquaculture and scientific research development funds; and the canonical correlation between forest area and GDP per capita. With the growth of economy and revenue and the steady development of agricultural production, there are still some serious problems, including the change of residents consumption is out of proportion to its material consumption; the socio-economic development depends on consuming of forests more heavily, which destroy the balance between the economic growth and environmental protection. [Conclusion] The results provide referential basis for the ecosystem study of Hebei Province.

Feature Fusion Multi-View Hashing Based on Random Kernel Canonical Correlation Analysis

Hashing technology has the advantages of reducing data storage and improving the efficiency of the learning system,making it more and more widely used in image retrieval.Multi-view data describes image information more comprehensively than traditional methods using a single-view.How to use hashing to combine multi-view data for image retrieval is still a challenge.In this paper,a multi-view fusion hashing method based on RKCCA(Random Kernel Canonical Correlation Analysis)is proposed.In order to describe image content more accurately,we use deep learning dense convolutional network feature DenseNet to construct multi-view by combining GIST feature or BoW_SIFT(Bag-of-Words model+SIFT feature)feature.This algorithm uses RKCCA method to fuse multi-view features to construct association features and apply them to image retrieval.The algorithm generates binary hash code with minimal distortion error by designing quantization regularization terms.A large number of experiments on benchmark datasets show that this method is superior to other multi-view hashing methods.

The Canonical Correlation Analysis on Semen Quality and Serum Heavy Metals in Chinese Young Men

Objective To explore the correlation between serum, heavy metal and semen quality in normal Chinese young menMethods This study was designed as a multi-center cross-sectional investigation. The subjects consisted of 562 male vomunteers who had undergone premarital physical examination in maternal and children health centers in 7 provinces in China. Results Results from Spearman rank correlation analysis (partial variable: region) show that serum lead and cadmium are negatively related to percentage of morphological normal sperm, but canonical correlation between semen quality and serum heavy metal are not significant. Canonical correlation analysis among the subjects from Guizhou shows cadmium is harmful to sperm morphology. In Henan, furthermore, results show lead and cadmium could negatively affect sperm viability and morphology. Conclusion Among all study subjects, canonical correlation between semen quality and serum heavy metal were not significant; however, results in some region showed serum cadmium and lead might be harmful to sperm quality.

Novel DDoS Feature Representation Model Combining Deep Belief Network and Canonical Correlation Analysis

Distributed denial of service(DDoS)attacks launch more and more frequently and are more destructive.Feature representation as an important part of DDoS defense technology directly affects the efficiency of defense.Most DDoS feature extraction methods cannot fully utilize the information of the original data,resulting in the extracted features losing useful features.In this paper,a DDoS feature representation method based on deep belief network(DBN)is proposed.We quantify the original data by the size of the network flows,the distribution of IP addresses and ports,and the diversity of packet sizes of different protocols and train the DBN in an unsupervised manner by these quantified values.Two feedforward neural networks(FFNN)are initialized by the trained deep belief network,and one of the feedforward neural networks continues to be trained in a supervised manner.The canonical correlation analysis(CCA)method is used to fuse the features extracted by two feedforward neural networks per layer.Experiments show that compared with other methods,the proposed method can extract better features.

Study on soil water characteristics of tobacco fields based on canonical correlation analysis

In order to identify the principal factors influencing soil water characteristics （SWC） and evaluate SWC effectively, the multivariate-statistical canonical correlation analysis （CCA） method was used to study and analyze the correlation between SWC and soil physical and chemical properties. Twenty-two soil samples were taken from 11 main tobacco-growing areas in Guizhou Province in China and the soil water characteristic curves （SWCC） and basic physical and chemical properties of the soil samples were determined. The results show that： （1） The soil bulk density, soil total porosity and soil capillary porosity have significant effects on SWC of tobacco fiels. Bulk density and total porosity are positively correlated with soil water retention characteristics （SWRC）, and soil capillary porosity is positively correlated with soil water supply characteristics （SWSC）. （2） Soil samples from different soil layers at the same soil sampling point show similarity or consistency in SWC. Inadequate soil water supply capability and imbalance between SWRC and SWSC are problems of tobacco soil. （3） The SWC of loamy clay are generally superior to those of silty clay loam.

SPATIAL REGULARIZATION OF CANONICAL CORRELATION ANALYSIS FOR LOW-RESOLUTION FACE RECOGNITION

Canonical correlation analysis ( CCA ) based methods for low-resolution ( LR ) face recognition involve face images with different resolutions ( or multi-resolutions ), i.e.LR and high-resolution ( HR ) .For single-resolution face recognition , researchers have shown that utilizing spatial information is beneficial to improving the recognition accuracy , mainly because the pixels of each face are not independent but spatially correlated.However , for a multi-resolution scenario , there are no related works.Therefore , a method named spatial regularization of canonical correlation analysis ( SRCCA ) is developed for LR face recognition to improve the performance of CCA by the regularization utilizing spatial information of different resolution faces.Furthermore , the impact of LR and HR spatial regularization terms on LR face recognition is analyzed through experiments.

Canonical correlation analysis to land-use structure and its driving forces——Taking Yulin Prefecture as an example

In this paper, one of the most classical statistical methods, Canonical Correlation Analysis (CCA) is applied to identify quantitatively the driving forces of landuse structure in Yulin Prefecture. The main analysis is carried out through the software SPSS with the data on the level of towns and townships in 1992. The results indicate that landuse structure is determined by comprehensive action of different factors. Landuse structure with rural characteristics is mainly determined by geographical factors such as the elevation, temperature and precipitation, while the landuse structure with urban characteristics is mainly determined by demographic and socioeconomic conditions. At the same time, tests were carried out through the canonical correlation coefficient and redundancy analysis.

Canonical Correlation Factors Causing the Formation of Accumulated Sand-belts along the Oasis Fringe in Hexi Corridor

Accumulated sand-belts refer to those formed along the oasis fringe,especially at the upwind location,due to the accumulation of sand blocked by farmland windbreak. In the 60 years since the foundation of new China,a lot of trees have been planted for desertification combating in northwest and north China,thus,accumulated sand-belts were formed at the upwind location. The formation and the ecological effects of the accumulated sand-belts along the oasis fringe is a new scientific concern. To study the formation causes of these belts in Hexi corridor,21 samples were selected,and the height / width of the belts,as well as the vegetation,soil,soil moisture and climatic factors were investigated. This paper analyzed the correlation between the height / width of the belts and the vegetation,soil,soil moisture and climatic factors using the methods of variance analysis,correlation analysis and canonical correlation analysis. The results indicate that: the accumulated sand-belts take a trend of being high and wide in the east whereas low and narrow in the west,and most of the parts tend to be stable; the species on the belts are dominated by Tamarix austromongolica,the vegetation cover and the pure vegetation cover of different dominant species on the leeward slope of the accumulated sand-belts vary significantly. The canonical correlation analysis shows that: the height and width of accumulated sand-belt is the interaction of precipitation,distance to the sand source,leeward vegetation cover and annual average wind speed. Moreover,the height of accumulated sand-belts are negatively correlated to the soil moisture at the depth of 30- 50 cm,air humidity and leeward vegetation cover,and the width of the belts is also negatively correlated with the distance to the sand source. The ecological effects of the accumulated sand-belts are both positive( stopping sands from moving into farmland,protective role as an obstacle)and negative( when the belts decay and activate one day,they will become the new sand sources). At present,there are no signs showing the negative effects of the belts. The ecological effects of the accumulated sand-belts are: firstly the protective role as an obstacle,and secondly to intercept and reduce the sands moving into farmlands.

ON USING NON-LINEAR CANONICAL CORRELATION ANALYSIS FOR VOICE CONVERSION BASED ON GAUSSIAN MIXTURE MODEL

Voice conversion algorithm aims to provide high level of similarity to the target voice with an acceptable level of quality.The main object of this paper was to build a nonlinear relationship between the parameters for the acoustical features of source and target speaker using Non-Linear Canonical Correlation Analysis(NLCCA) based on jointed Gaussian mixture model.Speaker indi-viduality transformation was achieved mainly by altering vocal tract characteristics represented by Line Spectral Frequencies(LSF).To obtain the transformed speech which sounded more like the target voices,prosody modification is involved through residual prediction.Both objective and subjective evaluations were conducted.The experimental results demonstrated that our proposed algorithm was effective and outperformed the conventional conversion method utilized by the Minimum Mean Square Error(MMSE) estimation.

Canonical Correlation Analysis and climate research

Correlation analysis as used by meteorologists and oceanographers is a tool for the analysisof the spacial or temporal variability of physical fields. In his notes, Dr. Hasselmann pro-posed to combine correlation analysis and linear regression analysis in climate prediction re-search. The main idea is to decompose the physical field into its principal oscillation patterns.

Multiple moving sources passive location based on multiset canonical correlation analysis

To solve the problem of multiple moving sources passive location, a novel blind source separa- tion （BSS） algorithm based on the muhiset canonical correlation analysis （MCCA） is presented by exploiting the different temporal structure of uncorrelated source signals first, and then on the basis of this algorithm, a novel multiple moving sources passive location method is proposed using time difference of arrival （TDOA） and frequency difference of arrival （FDOA） measurements. The key technique of this location method is TDOA and FDOA joint estimation, which is based on BSS. By blindly separating mixed signals from multiple moving sources, the multiple sources location problem can be translated to each source location in turn, and the effect of interference and noise can also he removed. The simulation results illustrate that the performance of the MCCA algorithm is very good with relatively light computation burden, and the location algorithm is relatively simple and effective.

Analysis of Input and Output of China's Agriculture Based on Canonical Correlation

I select effective irrigated area, consumption of agricultural chemical fertilizer, electricity consumed in rural areas, and total power of agricultural machinery as input variables of China's agriculture; I select grain, bean, tobacco, oil-bearing crop and fruit as output variables of China's agriculture. By using the data of China Statistical Yearbook in 2010, based on the analysis method of canonical correlation, I conduct research on the input and output of China's agriculture. The results show that consumption of chemical fertilizer has the biggest impact on the agricultural output of China, followed by the input of total power of agricultural machinery; the canonical variable of agricultural output of China is mainly impacted by grain, oil-bearing crop and fruit; in terms of the selected variables, the output increase of grain, oil-bearing crop and fruit in China arises from the input increase of agricultural chemical fertilizer and machinery, and there is high-degree correlation between the two. According to the conclusions, the policy suggestions are put forward as follows: gradually decrease consumption of chemical fertilizer; increase the use of modern agricultural machinery; increase agricultural irrigation input.

A NOVEL ALGORITHM FOR VOICE CONVERSION USING CANONICAL CORRELATION ANALYSIS

A novel algorithm for voice conversion is proposed in this paper. The mapping function of spectral vectors of the source and target speakers is calculated by the Canonical Correlation Analysis (CCA) estimation based on Gaussian mixture models. Since the spectral envelope feature remains a majority of second order statistical information contained in speech after Linear Prediction Coding (LPC) analysis, the CCA method is more suitable for spectral conversion than Minimum Mean Square Error (MMSE) because CCA explicitly considers the variance of each component of the spectral vectors during conversion procedure. Both objective evaluations and subjective listening tests are conducted. The experimental results demonstrate that the proposed scheme can achieve better per- formance than the previous method which uses MMSE estimation criterion.

A Novel 3D Gait Model for Subject Identification Robust against Carrying and Dressing Variations

Subject identification via the subject’s gait is challenging due to variations in the subject’s carrying and dressing conditions in real-life scenes.This paper proposes a novel targeted 3-dimensional(3D)gait model(3DGait)represented by a set of interpretable 3DGait descriptors based on a 3D parametric body model.The 3DGait descriptors are utilised as invariant gait features in the 3DGait recognition method to address object carrying and dressing.The 3DGait recognitionmethod involves 2-dimensional(2D)to 3DGaitdata learningbasedon3Dvirtual samples,a semantic gait parameter estimation Long Short Time Memory(LSTM)network(3D-SGPE-LSTM),a feature fusion deep model based on a multi-set canonical correlation analysis,and SoftMax recognition network.First,a sensory experiment based on 3D body shape and pose deformation with 3D virtual dressing is used to fit 3DGait onto the given 2D gait images.3Dinterpretable semantic parameters control the 3D morphing and dressing involved.Similarity degree measurement determines the semantic descriptors of 2D gait images of subjects with various shapes,poses and styles.Second,using the 2D gait images as input and the subjects’corresponding 3D semantic descriptors as output,an end-to-end 3D-SGPE-LSTM is constructed and trained.Third,body shape,pose and external gait factors(3D-eFactors)are estimated using the 3D-SGPE-LSTM model to create a set of interpretable gait descriptors to represent the 3DGait Model,i.e.,3D intrinsic semantic shape descriptor(3DShape);3D skeleton-based gait pose descriptor(3D-Pose)and 3D dressing with other 3D-eFators.Finally,the 3D-Shape and 3D-Pose descriptors are coupled to a unified pattern space by learning prior knowledge from the 3D-eFators.Practical research on CASIA B,CMU MoBo,TUM GAID and GPJATK databases shows that 3DGait is robust against object carrying and dressing variations,especially under multi-cross variations.

A Novel CCA-NMF Whitening Method for Practical Machine Learning Based Underwater Direction of Arrival Estimation

Underwater direction of arrival(DOA)estimation has always been a very challenging theoretical and practical problem.Due to the serious non-stationary,non-linear,and non-Gaussian characteristics,machine learning based DOA estimation methods trained on simulated Gaussian noised array data cannot be directly applied to actual underwater DOA estimation tasks.In order to deal with this problem,environmental data with no target echoes can be employed to analyze the non-Gaussian components.Then,the obtained information about non-Gaussian components can be used to whiten the array data.Based on these considerations,a novel practical sonar array whitening method was proposed.Specifically,based on a weak assumption that the non-Gaussian components in adjacent patches with and without target echoes are almost the same,canonical cor-relation analysis(CCA)and non-negative matrix factorization(NMF)techniques are employed for whitening the array data.With the whitened array data,machine learning based DOA estimation models trained on simulated Gaussian noised datasets can be used to perform underwater DOA estimation tasks.Experimental results illustrated that,using actual underwater datasets for testing with known machine learning based DOA estimation models,accurate and robust DOA estimation performance can be achieved by using the proposed whitening method in different underwater con-ditions.

Facial expression recognition based on fuzzy-LDA/CCA

A novel fuzzy linear discriminant analysis method by the canonical correlation analysis （fuzzy-LDA/CCA）is presented and applied to the facial expression recognition. The fuzzy method is used to evaluate the degree of the class membership to which each training sample belongs. CCA is then used to establish the relationship between each facial image and the corresponding class membership vector, and the class membership vector of a test image is estimated using this relationship. Moreover, the fuzzy-LDA/CCA method is also generalized to deal with nonlinear discriminant analysis problems via kernel method. The performance of the proposed method is demonstrated using real data.

Aggregate Characteristics During Natural Revegetation on the Loess Plateau

Field investigations and laboratory analysis were conducted to study the characteristics of soil water-stable aggregates during vegetation rehabilitation in typical grassland soils of the hilly-gullied loess area. The relationship between water- stable aggregates and other soil properties was analyzed using canonical correlation analysis and principal component analysis. The results show that during the natural revegetation, the aggregates 〉 5 mm dominated and constituted between 50% and 80% of the total soil water-stable aggregates in most of the soil layers. The 2-5 mm aggregate class was the second main component. The mean value of water-stable aggregates 〉 5 mm within the 0-2 m soil profile under different plant communities decreased in the following order： Stipa grandis 〉 Stipa bungeana Trin. 〉 Artemisia sacrorum Ledeb. 〉 Thymus mongolicus Ronn. 〉 Hierochloe odorata （L.） Beauv. Clay, organic matter, and total N were the key factors that influenced the water stability of the aggregates. Total N and organic matter were the main factors that affected the water stability of the aggregates 〉 5 mm and 0.5-1 mm in size. The contents of Fe2O3, Al2O3, and physical clay （〈 0.01 mm） were the main factors which affected the water stability of the 1-2 and 0.25-0.5 mm aggregates.

Spatial Heterogeneity of Soil Mineral Oxide Components in Depression Between Karst Hills,Southwest China

In karst regions,the spatial heterogeneity of soil mineral oxides and environmental variables is still not clear.We investigated the spatial heterogeneity of SiO2,Al2O3,Fe2O3,CaO,MgO,P2O5,K2O,and MnO contents in the soils of slope land,plantation forest,secondary forest,and primary forest,as well as their relationships with environmental variables in a karst region of Southwest China.Geostatistics,principal component analysis(PCA),and canonical correlation analysis(CCA)were applied to analyze the field data.The results show that SiO2was the predominant mineral in the soils(45.02%–67.33%),followed by Al2O3and Fe2O3.Most soil mineral oxide components had a strong spatial dependence,except for CaO,MgO,and P2O5in the plantation forest,MgO and P2O5in the secondary forest,and CaO in the slope land.Dimensionality reduction in PCA was not appropriate due to the strong spatial heterogeneity in the ecosystems.Soil mineral oxide components,the main factors in all ecosystems,had greater influences on vegetation than those of conventional soil properties.There were close relationships between soil mineral oxide components and vegetation,topography,and conventional soil properties.Mineral oxide components affected species diversity,organic matter and nitrogen levels.