Significant Vector Learning to Construct Sparse Kernel Regression Models

Junbin Gao, Daming Shi, Xiaomao Liu

    Research output: Contribution to journalArticlepeer-review

    21 Citations (Scopus)


    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
    Issue number7
    Publication statusPublished - 2007


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