Infinite max-margin factor analysis via data augmentation
Jointly learning FA and SVM, MMFA is proposed to get a discriminative subspace. Clustering the dataset in the subspace by DPM, MMFA is extended to iMMFA. Thanks to the jointly learning framework, they gain good prediction performance. Having the data description ability, the proposed models can reject outlier samples. In Bayesian framework, parameters can be inferred efficiently by the Gibbs sampler.
factor analysis latent variable support vector machine dirichlet process mixture classification and rejection performance