Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Computational Statistics & Data Analysis
Non-negative matrix factorization with α-divergence
Pattern Recognition Letters
Convex and Semi-Nonnegative Matrix Factorizations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-way Data Analysis and Blind Source Separation
IEEE Transactions on Neural Networks - Part 1
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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.