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ICMLA
2008

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

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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.
Theodoros Damoulas, Yiming Ying, Mark A. Girolami,
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2008
Where ICMLA
Authors Theodoros Damoulas, Yiming Ying, Mark A. Girolami, Colin Campbell
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