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PAKDD
2009
ACM

Dynamic Exponential Family Matrix Factorization

10 years 6 months ago
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.
Kohei Hayashi, Junichiro Hirayama, Shin Ishii
Added 20 May 2010
Updated 20 May 2010
Type Conference
Year 2009
Where PAKDD
Authors Kohei Hayashi, Junichiro Hirayama, Shin Ishii
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