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arXiv · 1602.06863

Higher-Order Low-Rank Regression

Guillaume Rabusseau·Hachem Kadri
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Abstract

This paper proposes an efficient algorithm (HOLRR) to handle regression tasks where the outputs have a tensor structure. We formulate the regression problem as the minimization of a least square criterion under a multilinear rank constraint, a difficult non convex problem. HOLRR computes efficiently an approximate solution of this problem, with solid theoretical guarantees. A kernel extension is also presented. Experiments on synthetic and real data show that HOLRR outperforms multivariate and multilinear regression methods and is considerably faster than existing tensor methods.

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BibTeXRIS

Guillaume Rabusseau, Hachem Kadri. 2016-02-22. Higher-Order Low-Rank Regression. https://arxiv.org/abs/1602.06863

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