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Low-Rank Regression with Tensor Responses
Guillaume Rabusseau · Hachem Kadri

Wed Dec 07 09:00 AM -- 12:30 PM (PST) @ Area 5+6+7+8 #107

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 computes accurate solutions while being computationally very competitive.

Author Information

Guillaume Rabusseau (Aix-Marseille University)
Hachem Kadri (Aix-Marseille University)

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