A New Feature Extraction Algorithm Based on Orthogonal Regularized Kernel CCA and Its Application. (29th October 2018)

• Record Type: Journal Article
• Authors: Guo, Xinchen; Fan, Xiuling; Xi, Xiantian; Zeng, Fugeng
• Other Names: Yang Jar Ferr Academic Editor.
• Is Part Of: Journal of electrical and computer engineering. Volume 2018(2018)
• Publication Date: 2018-10-29
• Subjects: Computer engineering -- Periodicals; Electrical engineering -- Periodicals; 621.3905
• DOI: 10.1155/2018/8745251
• Languages: English
• ISSNs: 2090-0147

Abstract

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.