L1 LASSO Modeling and Its Bayesian Inference

Authors:
Junbin Gao;Michael Antolovich;Paul W. Kwan
Affiliations:
School of Computer Science and Accounting, Charles Sturt University, Bathurst, Australia NSW 2795;School of Computer Science and Accounting, Charles Sturt University, Bathurst, Australia NSW 2795;School of Science and Technology, University of New England, Armidale, Australia NSW 2351
Venue:
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Year:
2008

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Abstract

A new iterative procedure for solving regression problems with the so-called LASSO penalty [1] is proposed by using generative Bayesian modeling and inference. The algorithm produces the anticipated parsimonious or sparse regression models that generalize well on unseen data. The proposed algorithm is quite robust and there is no need to specify any model 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.