Inferring Sparse Kernel Combinations and Relevance Vectors: An application to subcellular localization of proteins
Theodoros Damoulas, Yiming Ying, Mark Girolami and Colin Campbell
In: ICMLA2008, California, USA(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.

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