Sparse kernel learning with LASSO and Bayesian inference algorithm.

Gao, Junbin and Kwan, Paul W. and Shi, Daming (2010) Sparse kernel learning with LASSO and Bayesian inference algorithm. Neural Networks, 23 (2). pp. 257-264. ISSN 0893-6080

Full text is not in this repository.

Official URL: http://dx.doi.org/10.1016/j.neunet.2009.07.001

This item is available in the Library Catalogue

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 formula- tion 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.

Item Type:Article
Research Areas:Science & Technology > Science & Technology
Citations on ISI Web of Science:2
ID Code:6868
Useful Links:
Deposited On:18 Jan 2011 14:12
Last Modified:10 Oct 2013 09:22

Repository Staff Only: item control page

Full text downloads (NB count will be zero if no full text documents are attached to the record)

Downloads per month over the past year