A novel feature fusion method based on partial least squares regression

Authors:
Quan-Sen Sun;Zhong Jin;Pheng-Ann Heng;De-Shen Xia
Affiliations:
School of Science, Jinan University, Jinan, China;Department of Computer Science, Nanjing University of Science &Technology, Nanjing, China;Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong;Department of Computer Science, Nanjing University of Science &Technology, Nanjing, China
Venue:
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
Year:
2005

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Abstract

The partial least squares (PLS) regression is a new multivariate data analysis method. In this paper, based on the ideas of PLS model and feature fusion, a new non-iterative PLS algorithm and a novel method of feature fusion are proposed. The proposed method comprises three steps: firstly, extracting two sets of feature vectors with the same pattern and establishing PLS criterion function between the two sets of feature vectors; then, extracting two sets of PLS components by the PLS algorithm in this paper; and finally, doing feature fusion for classification by using two strategies. Experiment results on the ORL face image database and the Concordia University CENPARMI database of handwritten Arabic numerals show that the proposed method is efficient. Moreover, the proposed non-iterative PLS algorithm is superior to the existing iterative PLS algorithms on the computational cost and speed of feature extraction.