Title

The Relevance Sample-Feature Machine: A Sparse Bayesian Learning Approach to Joint Feature-Sample Selection

Authors

Authors

Y. Mohsenzadeh; H. Sheikhzadeh; A. M. Reza; N. Bathaee;M. M. Kalayeh

Comments

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Abbreviated Journal Title

IEEE T. Cybern.

Keywords

Joint feature selection and classifier design; relevance features; relevance sample feature machine (RSFM); relevance samples; sparse; Bayesian learning; VECTOR MACHINES; GAUSSIAN-PROCESSES; CLASSIFICATION; REGRESSION; TRACKING; Computer Science, Artificial Intelligence; Computer Science, Cybernetics

Abstract

This paper introduces a novel sparse Bayesian machine-learning algorithm for embedded feature selection in classification tasks. Our proposed algorithm, called the relevance sample feature machine (RSFM), is able to simultaneously choose the relevance samples and also the relevance features for regression or classification problems. We propose a separable model in feature and sample domains. Adopting a Bayesian approach and using Gaussian priors, the learned model by RSFM is sparse in both sample and feature domains. The proposed algorithm is an extension of the standard RVM algorithm, which only opts for sparsity in the sample domain. Experimental comparisons on synthetic as well as benchmark data sets show that RSFM is successful in both feature selection (eliminating the irrelevant features) and accurate classification. The main advantages of our proposed algorithm are: less system complexity, better generalization and avoiding overfitting, and less computational cost during the testing stage.

Journal Title

Ieee Transactions on Cybernetics

Volume

43

Issue/Number

6

Publication Date

1-1-2013

Document Type

Article

Language

English

First Page

2241

Last Page

2254

WOS Identifier

WOS:000327647500060

ISSN

2168-2267

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