Please use this identifier to cite or link to this item:
https://hdl.handle.net/1959.11/7673
Title: | Sparse Kernel Learning with LASSO and Its Bayesian Inference Algorithm | Contributor(s): | Gao, Junbin (author); Kwan, Paul H (author); Shi, Daming (author) | Publication Date: | 2010 | DOI: | 10.1016/j.neunet.2009.07.001 | Handle Link: | https://hdl.handle.net/1959.11/7673 | Abstract: | Kernelized LASSO (Least Absolute Selection and Shrinkage Operator) has been investigated in two separate recent papers (Gao et al., 2008) and (Wang et al., 2007). This paper is concerned with learning kernels under the LASSO formulation via adopting a generative Bayesian learning and inference approach. A new robust learning algorithm is proposed which produces a sparse kernel model with the capability of learning regularized parameters and kernel hyperparameters. A comparison with state-of-the-art methods for constructing sparse regression models such as the relevance vector machine (RVM) and the local regularization assisted orthogonal least squares regression (LROLS) is given. The new algorithm is also demonstrated to possess considerable computational advantages. | Publication Type: | Journal Article | Source of Publication: | Neural Networks, 23(2), p. 257-264 | Publisher: | Pergamon Press | Place of Publication: | United Kingdom | ISSN: | 1879-2782 0893-6080 |
Fields of Research (FoR) 2008: | 080205 Numerical Computation 080201 Analysis of Algorithms and Complexity 080108 Neural, Evolutionary and Fuzzy Computation |
Socio-Economic Objective (SEO) 2008: | 890202 Application Tools and System Utilities 899999 Information and Communication Services not elsewhere classified |
Peer Reviewed: | Yes | HERDC Category Description: | C1 Refereed Article in a Scholarly Journal |
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Appears in Collections: | Journal Article |
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