Although very widely used in unsupervised data mining, most clustering methods are affected by the instability of the resulting clusters w.r.t. the initialization of the algorithm ...
Non-negative Matrix Factorization (NMF) and Probabilistic Latent Semantic Indexing (PLSI) have been successfully applied to document clustering recently. In this paper, we show th...
We derive the clustering problem from first principles showing that the goal of achieving a probabilistic, or ”hard”, multi class clustering result is equivalent to the algeb...
Nonnegative matrix tri-factorization (NMTF) is a 3-factor decomposition of a nonnegative data matrix, X USV , where factor matrices, U, S, and V , are restricted to be nonnegativ...
We introduce the posterior probabilistic clustering (PPC), which provides a rigorous posterior probability interpretation for Nonnegative Matrix Factorization (NMF) and removes th...