We discuss optimal low-rank approximation of matrices with non-negative entries, without the need of a regularization parameter. It will be shown that the standard SVD-approximation can be recovered via convex-optimization, which is why adding mild convex constraints often gives an optimal solution. Moreover, the issue of computability will be a...
Creator:
Grussler, Christian (Lund University)
Created:
2016-02-01
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.