Sparse Group Inductive Matrix Completion
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by
Ivan Nazarov, Boris Shirokikh, Maria Burkina, Gennady Fedonin and
Maxim Panov
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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