Non-Gaussian Discriminative Factor Models via the Max-Margin Rank-Likelihood
Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1254-1263, 2015.
We consider the problem of discriminative factor analysis for data that are in general non-Gaussian. A Bayesian model based on the ranks of the data is proposed. We first introduce a max-margin version of the rank-likelihood. A discriminative factor model is then developed, integrating the new max-margin rank-likelihood and (linear) Bayesian support vector machines, which are also built on the max-margin principle. The discriminative factor model is further extended to the nonlinear case through mixtures of local linear classifiers, via Dirichlet processes. Fully local conjugacy of the model yields efficient inference with both Markov Chain Monte Carlo and variational Bayes approaches. Extensive experiments on benchmark and real data demonstrate superior performance of the proposed model and its potential for applications in computational biology.