Dynamic Exponential Family Matrix Factorization

Abstract. We propose a new approach to modeling time-varying relational data such as e-mail transactions based on a dynamic extension of matrix factorization. To estimate effectively the true relationships behind a sequence of noise-corrupted relational matrices, their dynamic evolutions are modeled in a space of low-rank matrices. The observed matrices are assumed as to be sampled from an exponential family distribution that has the low-rank matrix as natural parameters. We apply the sequential Bayesian framework to track the variations of true parameters. In the experiments using both artificial and real-world datasets, we demonstrate our method can appropriately estimate time-varying true relations based on noisy observations, more effectively than existing methods.

• Type: Conference
• Year: 2009
• Where: PAKDD
• Authors: Kohei Hayashi, Junichiro Hirayama, Shin Ishii
• Keywords: Data Mining, Low-rank Matrices, Noise-corrupted Relational Matrices, PAKDD 2009, Time-varying Relational Data