Matrix Factorization for Evolution Data

We study a matrix factorization problem, that is, to find two factor matrices U and V such that R≈UT×V, where R is a matrix composed of the values of the objects O1,O2,…,On at consecutive time points T1,T2,…,Tt. We first present MAFED, a constrained optimization model for this problem, which straightforwardly performs factorization on R. Then based on the interplay of the data in U, V, and R, a probabilistic graphical model using the same optimization objects is constructed, in which structural dependencies of the data in these matrices are revealed. Finally, we present a fitting algorithm to solve the proposed MAFED model, which produces the desired factorization. Empirical studies on real-world datasets demonstrate that our approach outperforms the state-of-the-art comparison algorithms.