Successively alternate least square for low-rank matrix factorization with bounded missing data
The problem of low-rank matrix factorization with missing data has attracted many significant attention in the fields related to computer vision. The previous model mainly minimizes the total errors of the recovered low-rank matrix on observed entries. It may produce an optimal solution with less physical meaning. This paper gives a theoretical analysis of the sensitivities of the original model and proposes a modified constrained model and iterative methods for solving the constrained problem. We show that solutions of original model can be arbitrarily far from each others. Two kinds of sufficient conditions of this catastrophic phenomenon are given. In general case, we also give a low bound of error between an ?-optimal solution that is practically obtained in computation and a theoretically optimal solution. A constrained model on missing entries is considered for this missing data problem. We propose a two-step projection method for solving the constrained problem. We also modify the method by a successive alternate technique. The proposed algorithm, named as SALS, is easy to implement, as well as converges very fast even for a large matrix. Numerical experiments on simulation data and real examples are given to illuminate the algorithm behaviors of SALS.
low-rank matrix matrix factorization missing data computer vision matrix complement 3d reconstruction