An improved sequential method for principal component analysis

Authors:
Ze Wang;Yin Lee;Simone Fiori;Chi-Sing Leung;Yi-Sheng Zhu
Affiliations:
Department of Biomedical Engineering, Shanghai Jiao Tong University, HuaShan RD. 1954, Shanghai 200030, China;Department of Biomedical Engineering, Shanghai Jiao Tong University, HuaShan RD. 1954, Shanghai 200030, China;Department of Industrial Engineering, University of Perugia, Italy;Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China;Department of Biomedical Engineering, Shanghai Jiao Tong University, HuaShan RD. 1954, Shanghai 200030, China
Venue:
Pattern Recognition Letters
Year:
2003

Citing 2
Cited 1

A Practical Sequential Method for Principal Component Analysis

Neural Processing Letters
Principal component extraction using recursive least squares learning

IEEE Transactions on Neural Networks

Classifying functional time series

Signal Processing

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Abstract

In sequential principal component (PC) extraction, when increasing numbers of PCs are extracted the accumulated extraction error becomes dominant and makes a reliable extraction of the remaining PCs difficult. This paper presents an improved cascade recursive least squares method for PCs' extraction. The good features of the proposed approach are illustrated through simulation results, and include improved convergence speed and higher extraction accuracy.