Sparse Group Inductive Matrix Completion
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We consider the problem of inductive matrix completion under the assumption that many features are non-informative, which leads to row- and column-sparse structure of coefficient matrix. Under the additional assumption on the low rank of coefficient matrix we propose the matrix factorization framework with group-lasso regularization on parameter matrices. We suggest efficient optimization algorithm for the solution of the obtained problem. From theoretical point of view, we prove the oracle generalization bound on the expected error of matrix completion. Corresponding sample complexity bounds show the benefits of the proposed approach compared to competitors in the sparse problems. The experiments on synthetic and real-world datasets show the state-of-the-art efficiency of the proposed method.
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