Max-Margin Similarity Preserving Factor Analysis Via Gibbs Sampling
Hua Bu Chen, Bo Chen, Wei Hong Liu, Feng Xue Zhang

In this paper, we develop the max-margin similarity preserving factor analysis (MMSPFA) model. MMSPFA utilizes the latent variable support vector machine (LVSVM) as the classification criterion in the latent space to learn a discriminative subspace with max-margin constraint. It jointly learns factor analysis (FA) model, similarity preserving (SP) term and max-margin classifier in a united Bayesian framework to improve the prediction performance. Thanks to the conditionally conjugate property, the parameters in our model can be inferred via the simple and efficient Gibbs sampler. Finally, we test our methods on real-world data to demonstrate their efficiency and effectiveness.