On Weighted Low-Rank Approximation

Abstract

Our main interest is the low-rank approximation of a matrix in R^m.n under a weighted Frobenius norm. This norm associates a weight to each of the (m x n) matrix entries. We conjecture that the number of approximations is at most min(m, n). We also investigate how the approximations depend on the weight-values.

13 pages

References in corpus (3)

• A Simpler Approach to Matrix Completion
• Low-Rank Matrix Approximation with Weights or Missing Data is NP-hard
• Variable projection for affinely structured low-rank approximation in weighted 2-norms