Inferring Sparse Kernel Combinations and Relevance Vectors: An Application to Subcellular Localization of Proteins

In this paper, we introduce two new formulations for multi-class multi-kernel relevance vector machines (mRVMs) 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.

• Type: Conference
• Year: 2008
• Where: ICMLA
• Authors: Theodoros Damoulas, Yiming Ying, Mark A. Girolami, Colin Campbell
• Keywords: ICMLA 2008, Machine Learning, Multi-class Multi-kernel Relevance, Multi-feature Multinomial Classification, Training Samples