Significant Vector Learning to Construct Sparse Kernel Regression Models

Junbin Gao, Daming Shi, Xiaomao Liu

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)791-798
Number of pages8
JournalNeural Networks
Volume20
Issue number7
DOIs
Publication statusPublished - 2007

Fingerprint Dive into the research topics of 'Significant Vector Learning to Construct Sparse Kernel Regression Models'. Together they form a unique fingerprint.

Cite this