Significant vector learning to construct sparse kernel regression models

Authors:
Junbin Gao;Daming Shi;Xiaomao Liu
Affiliations:
School of Computer Science, Charles Sturt University, Bathurst, NSW 2795, Australia;School of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore;Department of Mathematics, Huazhong University of Science and Technology, Wuhan 430074, China
Venue:
Neural Networks
Year:
2007

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Abstract

A novel significant vector (SV) regression algorithm is proposed in this paper based on an analysis of Chen's orthogonal least squares (OLS) regression algorithm. The proposed regularized SV algorithm finds the significant vectors in a successive greedy process in which, compared to the classical OLS algorithm, the orthogonalization has been removed from the algorithm. The performance of the proposed algorithm is comparable to the OLS algorithm while it saves a lot of time complexities in implementing the orthogonalization needed in the OLS algorithm.