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A Unified Framework for Structured Low-rank Matrix Learning
Pratik Kumar Jawanpuria · Bamdev Mishra
We consider the problem of learning a low-rank matrix, constrained to lie in a linear subspace, and introduce a novel factorization for modeling such matrices. A salient feature of the proposed factorization scheme is it decouples the low-rank and the structural constraints onto separate factors. We formulate the optimization problem on the Riemannian spectrahedron manifold, where the Riemannian framework allows to develop computationally efficient conjugate gradient and trust-region algorithms. Experiments on problems such as standard/robust/non-negative matrix completion, Hankel matrix learning and multi-task learning demonstrate the efficacy of our approach.
Author Information
Pratik Kumar Jawanpuria (Microsoft)
Bamdev Mishra (Microsoft)
Related Events (a corresponding poster, oral, or spotlight)
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2018 Poster: A Unified Framework for Structured Low-rank Matrix Learning »
Fri Jul 13th 04:15 -- 07:00 PM Room Hall B
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