Title

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

Keywords

Joint feature selection and classifier design; Relevance features; Relevance sample feature machine (RSFM); Relevance samples; Sparse Bayesian learning

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. © 2013 IEEE.

Publication Date

12-1-2013

Publication Title

IEEE Transactions on Cybernetics

Volume

43

Issue

6

Number of Pages

2241-2254

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/TCYB.2013.2260736

Socpus ID

84890054595 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84890054595

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