Exact Matrix Completion via Convex Optimization

Details Exact Matrix Completion via Convex Optimization Exact Matrix Completion via Convex Optimization

2008-05-29

arxiv.org/abs/0805.4471v1

Computer Science

Information Theory

Scientific paper

We consider a problem of considerable practical interest: the recovery of a data matrix from a sampling of its entries. Suppose that we observe m entries selected uniformly at random from a matrix M. Can we complete the matrix and recover the entries that we have not seen? We show that one can perfectly recover most low-rank matrices from what appears to be an incomplete set of entries. We prove that if the number m of sampled entries obeys m >= C n^{1.2} r log n for some positive numerical constant C, then with very high probability, most n by n matrices of rank r can be perfectly recovered by solving a simple convex optimization program. This program finds the matrix with minimum nuclear norm that fits the data. The condition above assumes that the rank is not too large. However, if one replaces the 1.2 exponent with 1.25, then the result holds for all values of the rank. Similar results hold for arbitrary rectangular matrices as well. Our results are connected with the recent literature on compressed sensing, and show that objects other than signals and images can be perfectly reconstructed from very limited information.

Affiliated with

Also associated with

No associations

Rating

Exact Matrix Completion via Convex Optimization does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Exact Matrix Completion via Convex Optimization, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Exact Matrix Completion via Convex Optimization will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-178955