Non-Gaussian Discriminative Factor Models via the Max-Margin Rank-Likelihood
This work addresses the challenge of handling non-Gaussian data in discriminative factor analysis, which is incremental as it builds on existing methods like rank-likelihood and support vector machines.
The paper tackles the problem of discriminative factor analysis for non-Gaussian data by proposing a Bayesian model based on a new max-margin rank-likelihood, integrating it with Bayesian support vector machines and extending it to nonlinear cases via Dirichlet processes. The model demonstrates superior performance in experiments on benchmark and real data, with potential applications in computational biology.
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 new {\em max-margin} version of the rank-likelihood. A discriminative factor model is then developed, integrating the 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 {\em 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.