TY - GEN

T1 - Matrix completion from noisy entries

AU - Keshavan, Raghunandan H.

AU - Montanari, Andrea

AU - Oh, Sewoong

PY - 2009/12/1

Y1 - 2009/12/1

N2 - Given a matrix M of low-rank, we consider the problem of reconstructing it from noisy observations of a small, random subset of its entries. The problem arises in a variety of applications, from collaborative filtering (the 'Netflix problem') to structure-from-motion and positioning. We study a low complexity algorithm introduced in [1], based on a combination of spectral techniques and manifold optimization, that we call here OPTSPACE. We prove performance guarantees that are order-optimal in a number of circumstances.

AB - Given a matrix M of low-rank, we consider the problem of reconstructing it from noisy observations of a small, random subset of its entries. The problem arises in a variety of applications, from collaborative filtering (the 'Netflix problem') to structure-from-motion and positioning. We study a low complexity algorithm introduced in [1], based on a combination of spectral techniques and manifold optimization, that we call here OPTSPACE. We prove performance guarantees that are order-optimal in a number of circumstances.

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M3 - Conference contribution

AN - SCOPUS:84858742642

SN - 9781615679119

T3 - Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference

SP - 952

EP - 960

BT - Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference

T2 - 23rd Annual Conference on Neural Information Processing Systems, NIPS 2009

Y2 - 7 December 2009 through 10 December 2009

ER -