Inferring Sparse Kernel Combinations and Relevance Vectors: An application to subcellular localization of proteins

Damoulas Theodoros, Ying Yiming, ICG Campbell, Girolami Mark

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

30 Citations (Scopus)

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.
Translated title of the contributionInferring Sparse Kernel Combinations and Relevance Vectors: An application to subcellular localization of proteins
Original languageEnglish
Title of host publicationInternational Conference on Machine Learning and Applications (ICMLA)
Number of pages6
Publication statusPublished - 2008

Bibliographical note

Name and Venue of Event: ICMLA, San Diego, California
Conference Proceedings/Title of Journal: ICMLA 2008

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