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2005 International Conference on Neural Networks and Brain

DOI: 10.1109/icnnb.2005.1615001

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Sparse Kernel Regression Modelling Based on L1 Significant Vector Learning

Proceedings article published in 2005 by Junbin Gao ORCID, Darning Shi, Daming Shi
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This paper is available in a repository.

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Abstract

A novel L1 significant vector (SV) regression algorithm is proposed in the paper. The proposed regularized L1 SV algorithm finds the significant vectors in a successive greedy process. The performance of the proposed algorithm is comparable to the OLS algorithm while it saves a lot of time complexities in implementing orthogonalization needed in the OLS algorithm