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.

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 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.

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.

An extended binary subband canonical correlation analysis detection algorithm oriented to the radial contraction-expansion motion steady- state visual evoked paradigm

The radial contraction-expansion motion paradigm is a novel steady-state visual evoked experimental paradigm,and the electroencephalography(EEG)evoked potential is different from the traditional luminance modulation paradigm.The signal energy is concentrated chiefly in the fundamental frequency,while the higher harmonic power is lower.Therefore,the conventional steady-state visual evoked potential recognition algorithms optimizing multiple harmonic response components,such as the extended canonical correlation analysis(eCCA)and task-related component analysis(TRCA)algorithm,have poor recognition performance under the radial contraction-expansion motion paradigm.This paper proposes an extended binary subband canonical correlation analysis(eBSCCA)algorithm for the radial contraction-expansion motion paradigm.For the radial contraction-expansion motion paradigm,binary subband filtering was used to optimize the weighting coefficients of different frequency response signals,thereby improving the recognition performance of EEG signals.The results of offline experiments involving 13 subjects showed that the eBSCCA algorithm exhibits a better performance than the eCCA and TRCA algorithms under the stimulation of the radial contraction-expansion motion paradigm.In the online experiment,the average recognition accuracy of 13 subjects was 88.68%±6.33%,and the average information transmission rate(ITR)was 158.77±43.67 bits/min,which proved that the algorithm had good recognition effect signals evoked by the radial contraction-expansion motion paradigm.

Canonical correlation analysis of hydrological response and soil erosion under moving rainfall

The impacts of rainfall direction on the degree of hydrological response to rainfall properties were investigated using comparative rainfall-runoff experiments on a small-scale slope(4 m×1 m),as well as canonical correlation analysis(CCA).The results of the CCA,based on the observed data showed that,under conditions of both upstream and downstream rainfall movements,the hydrological process can be divided into instantaneous and cumulative responses,for which the driving forces are rainfall intensity and total rainfall,and coupling with splash erosion and wash erosion,respectively.The response of peak runoff(Pr)to intensity-dominated rainfall action appeared to be the most significant,and also runoff(R)to rainfall-dominated action,both for upstream-and downstream-moving conditions.Furthermore,the responses of sediment erosion in downstream-moving condition were more significant than those in upstream-moving condition.This study indicated that a CCA between rainfall and hydrological characteristics is effective for further exploring the rainfall-runoff-erosion mechanism under conditions of moving rainfall,especially for the downstream movement condition.

EXTREMES OF RATIOS OF DETERMINANTS AND CANONICAL CORRELATION VARIABLES

This article develops some extremes of the ratios of determinants. The results are themultivariate extensions of the extremes of quadratic forms, and can be applied to finding thecanonicai correlation variables of two random vectors. Hence a group of canonical correlationvariables is a solution of the extreme of the ratio of determinants.

MODEL SELECTION FOR CANONICAL CORRELATION ANALYSIS

In this paper, we study a general criterion for estimating the rank of canonical correlation matrix (CCM). Besides the strong consistency, we give the exponential-type bounds under certain conditions on the probability of wrong detection of the rank of CCM. On the basis of this criterion, we give two methods to determine the multiplicities of canonical correlation coefficents. And the strong consistency of them is established.

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.

Canonical corelation 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.

Genetic Correlation between Agronomically Important Traits in Yellow Passion Fruit

Breeding programs have sought to determine the minimum period of genotype evaluation, which is a long term-process. The objective of this work was to determine the association between qualitative traits of yellow passion fruit (Passiflora edulis Sims) and relate maturity to other characteristics. To carry out this work, 113 families of full-sibs and half-sibs were planted in Viçosa (MG), Brazil, in a randomized block experimental design arranged in sets (treatment groups). The association between the analyzed characteristics was studied using path analysis and canonical correlation techniques. It was found that lower-yielding but larger-fruit bearing genotypes of passion fruit plant also require more days until reaching anthesis. Round-shaped fruits are linked to higher contents of total soluble solids. More intense pulp color also indicates higher contents of total soluble solids. The riper the fruit, the more intensely orange is the color of the pulp. The ratio between the levels of total soluble solids and total titrimetric acidity was the main determinant of pulp color for passion fruit.

A Method of SSVEP Signal Identification Based on Improved eCAA

Brain-computer interfaces(BCI)based on steady-state visual evoked potentials(SSVEP)have attracted great interest because of their higher signal-to-noise ratio,less training,and faster information transfer.However,the existing signal recognition methods for SSVEP do not fully pay attention to the important role of signal phase characteristics in the recognition process.Therefore,an improved method based on extended Canonical Correlation Analysis(eCCA)is proposed.The phase parameters are added from the stimulus paradigm encoded by joint frequency phase modulation to the reference signal constructed from the training data of the subjects to achieve phase constraints on eCCA,thereby improving the recognition performance of the eCCA method for SSVEP signals,and transmit the collected signals to the robotic arm system to achieve control of the robotic arm.In order to verify the effectiveness and advantages of the proposed method,this paper evaluated the method using SSVEP signals from 35 subjects.The research shows that the proposed algorithm improves the average recognition rate of SSVEP signals to 82.76%,and the information transmission rate to 116.18 bits/min,which is superior to TRCA and traditional eCAA-based methods in terms of information transmission speed and accuracy,and has better stability.

矩阵方程的最小二乘{P,Q,k+1}-自反解

Let P∈C^( m×m )and Q∈C^( n×n) be Hermitian and{k+1}-potent matrices,i.e.,P k+1=P=P∗,Qk+1=Q=Q∗,where(·)∗stands for the conjugate transpose of a matrix.A matrix X∈C m×n is called{P,Q,k+1}-reflexive(anti-reflexive)if P XQ=X(P XQ=−X).In this paper,the least squares solution of the matrix equation AXB=C subject to{P,Q,k+1}-reflexive and anti-reflexive constraints are studied by converting into two simpler cases:k=1 and k=2.

Multi-view dimensionality reduction via canonical random correlation analysis

Canonical correlation analysis （CCA） is one of the most well-known methods to extract features from multi- view data and has attracted much attention in recent years. However, classical CCA is unsupervised and does not take discriminant information into account. In this paper, we add discriminant information into CCA by using random cross- view correlations between within-class samples and propose a new method for multi-view dimensionality reduction called canonical random correlation analysis （RCA）. In RCA, two approaches for randomly generating cross-view correlation samples are developed on the basis of bootstrap technique. Furthermore, kernel RCA （KRCA） is proposed to extract nonlinear correlations between different views. Experiments on several multi-view data sets show the effectiveness of the proposed methods.