A New Feature Extraction Algorithm Based on Orthogonal Regularized Kernel CCA and Its Application

Xinchen Guo,Xiuling Fan,Xiantian Xi,Fugeng Zeng

doi:10.1155/2018/8745251

Abstract

In this paper, an orthogonal regularized kernel canonical correlation analysis algorithm (ORKCCA) is proposed. ORCCA algorithm can deal with the linear relationships between two groups of random variables. But if the linear relationships between two groups of random variables do not exist, the performance of ORCCA algorithm will not work well. Linear orthogonal regularized CCA algorithm is extended to nonlinear space by introducing the kernel method into CCA. Simulation experimental results on both artificial and handwritten numerals databases show that the proposed method outperforms ORCCA for the nonlinear problems.

Highlights

Canonical correlation analysis (CCA) is a technique of multivariate statistical analysis, which deals with the mutual relationships of two sets of variables [1,2,3]. is method extracts the representative variables which are the linear combination of the variables in each group. e relationships between new variables can reflect the overall relationships between two groups of variables [4]
E orthogonal regularization canonical correlation analysis (ORCCA) algorithm [5] is that the original formula of CCA algorithm with orthogonal constraints is substituted for CCA conjugate orthogonalization [6, 7]
ORCCA algorithm is the same as CCA algorithm that both their goals look for the linear combinations of the variables in each group

Summary

Introduction

Canonical correlation analysis (CCA) is a technique of multivariate statistical analysis, which deals with the mutual relationships of two sets of variables [1,2,3]. is method extracts the representative variables which are the linear combination of the variables in each group. e relationships between new variables can reflect the overall relationships between two groups of variables [4]. Is method extracts the representative variables which are the linear combination of the variables in each group. ORCCA algorithm is the same as CCA algorithm that both their goals look for the linear combinations of the variables in each group. When the nonlinear relationships between the variables exist, ORCCA algorithm cannot extract effectively the comprehensive variables. The kernel method [9,10,11] is introduced into ORCCA algorithm, and ORKCCA algorithm is presented. In the higher-dimensional space, the characteristics of the data can be extracted and analyzed through the linear method. The computation of the orthogonal regularization canonical correlation analysis extends to a nonlinear feature space. Experimental results show that the accuracies of classification of our method in the nonlinear space are significantly improved. e experimental results show ORKCCA is feasible

Orthogonal Regularized CCA Algorithm

Simulation Experiments

Conclusions