Clipped Matrix Completion: a Remedy for Ceiling Effects

by   Takeshi Teshima, et al.

We consider the recovery of a low-rank matrix from its clipped observations. Clipping is a common prohibiting factor in many scientific areas that obstructs statistical analyses. On the other hand, matrix completion (MC) methods can recover a low-rank matrix from various information deficits by using the principle of low-rank completion. However, the current theoretical guarantees for low-rank MC do not apply to clipped matrices, as the deficit depends on the underlying values. Therefore, the feasibility of clipped matrix completion (CMC) is not trivial. In this paper, we first provide a theoretical guarantee for an exact recovery of CMC by using a trace norm minimization algorithm. Furthermore, we introduce practical CMC algorithms by extending MC methods. The simple idea is to use the squared hinge loss in place of the squared loss well used in MC methods for reducing the penalty of over-estimation on clipped entries. We also propose a novel regularization term tailored for CMC. It is a combination of two trace norm terms, and we theoretically bound the recovery error under the regularization. We demonstrate the effectiveness of the proposed methods through experiments using both synthetic data and real-world benchmark data for recommendation systems.


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