Fusing facial feature recognition algorithm based on kernel canonical correlation analysis

Second, we have proven the equivalence between a new implementation of kernel canonical correlation analysis and traditional implementation with less condition, and use it as the classifier of steganalysis, the effect is better than support vector mach

In this paper, reduced and important further features are obtained by introducing Kernel Canonical Correlation Analysis(KCCA), and the binary tree based multi-class classification SVM is used to complete the classification task by these further features.

Kernel Canonical Correlation Analysis(KCCA) is a recently addressed supervised machine learning methods,which is a powerful approach of extracting nonlinear features.

Such new variable is tested by pattern recognition for saturated alcohol and ether, combined with block variables' orthogonal computation and canonical correlation ana.

By the parent-offspring canonical correlation analysis(CCA)on sheep s characters of wool yield and wool quality of Chinese Merino(Xinjiang type),the results are as follows:1.

In order to choose the traits acting main function and even more efficaciously directing breeding,the canonical correlation among main traits of 247 specific line ZFD was conducted applying with Cancorr program of canonical correlation module of SPSS in this experiment.

By analyzing the relativity between two-dimensional maximum scatter-difference discriminant analysis(2DMSLDA) and two-dimensional Fisher discriminant analysis(2DFLDA),according to traditional canonical correlation analysis(CCA),a novel method of combining different feature matrixes directly is proposed in this paper by using the main idea of image projection in face recognition.

According to the traditional Canonical Correlation Analysis(CCA),a novel method of combined feature extraction called Two-Dimensional Canonical Correlation Analysis(2DCCA) was proposed in this paper.

Then according to the algorithm of canonical correlation analysis(CCA),proposed a kind of associated feature based on ear and face profile for classification and recognition.

By means of multivariate statistical analysis such as canonical analysis, correlation analysis and regression analysis etc .

Based on the Canonical Correlation Analysis(CCA),the temporal correlation and spatial correlation are used together to detect the activated area in the fMRI data.

Canonical Correlation Analysis(CCA) is applied to analyze the land use change and external driving factors,by which the cont.

To deal with how to characterize the gait with non-linearity correlation information,combined the Canonical Correlation Analysis with the locality method,the Locality Discriminant CCA is used to the gait recognition.