Sparse multinomial logistic regression via Bayesian L1 regularisation

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150 Citations (Scopus)

Abstract

Multinomial logistic regression provides the standard penalised maximum likelihood solution to multi-class pattern recognition problems. More recently, the development of sparse multinomial logistic regression models has found application in text processing and microarray classification, where explicit identification of the most informative features is of value. In this paper, we propose a sparse multinomial logistic regression method, in which the sparsity arises from the use of a Laplace prior, but where the usual regularisation parameter is integrated out analytically. Evaluation over a range of benchmark datasets reveals this approach results in similar generalisation performance to that obtained using cross-validation, but at greatly reduced computational expense.
Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems
EditorsBernhard Schölkopf, John Platt, Thomas Hofmann
PublisherMIT Press
Pages209-216
Number of pages8
Volume19
ISBN (Print)9780262195683
Publication statusPublished - 2007

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