PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Inferring sparse kernel combination and relevance vectors: an application to subcelluar localization of proteins
Theo Damoulas, Yiming Ying, Mark Girolami and Colin Campbell
Proceedings of the 7th International Conference on Machine Learning and Applications (ICMLA2008), 2008.

Abstract

In this paper, we introduce two new formulations for multi-class multi-kernel relevance vector machines (m- RVMs) that explicitly lead to sparse solutions, both in samples and in number of kernels. This enables their application to large-scale multi-feature multinomial classification problems where there is an abundance of training samples, classes and feature spaces. The proposed methods are based on an expectation-maximization (EM) framework employing a multinomial probit likelihood and explicit pruning of non-relevant training samples. We demonstrate the methods on a low-dimensional artificial dataset. We then demonstrate the accuracy and sparsity of the method when applied to the challenging bioinformatics task of predicting protein subcellular localization.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
ID Code:4634
Deposited By:Yiming Ying
Deposited On:13 March 2009