Sparse Group Inductive Matrix Completion release_4sl4wwou45f4ld43eq7b6utluy

by Ivan Nazarov, Boris Shirokikh, Maria Burkina, Gennady Fedonin and Maxim Panov

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2018  

Abstract

We consider the problem of matrix completion with side information (inductive matrix completion). In real-world applications many side-channel features are typically non-informative making feature selection an important part of the problem. We incorporate feature selection into inductive matrix completion by proposing a matrix factorization framework with group-lasso regularization on side feature parameter matrices. We demonstrate, that the theoretical sample complexity for the proposed method is much lower compared to its competitors in sparse problems, and propose an efficient optimization algorithm for the resulting low-rank matrix completion problem with sparsifying regularizers. Experiments on synthetic and real-world datasets show that the proposed approach outperforms other methods.
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Date   2018-10-06
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Language   en ?
arXiv  1804.10653v2
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