Pairwise clustering with t-PLSI

In the past decade, Probabilistic Latent Semantic Indexing (PLSI) has become an important modeling technique, widely used in clustering or graph partitioning analysis. However, the original PLSI is designed for multinomial data and may not handle other data types. To overcome this restriction, we generalize PLSI to t-exponential family based on a recently proposed information criterion called t-divergence. The t-divergence enjoys more flexibility than KL-divergence in PLSI such that it can accommodate more types of noise in data. To optimize the generalized learning objective, we propose a Majorization-Minimization algorithm which multiplicatively updates the factorizing matrices. The new method is verified in pairwise clustering tasks. Experimental results on real-world datasets show that PLSI with t-divergence can improve clustering performance in purity for certain datasets.