Sparse Multinomial Logistic Regression via Bayesian L1 Regularisation
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 have 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 parameters are 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.