Non-Convex Rank/Sparsity Regularization and Local Minima

Carl Olsson, Marcus Carlsson, Fredrik Andersson, Viktor Larsson; The IEEE International Conference on Computer Vision (ICCV), 2017, pp. 332-340


This paper considers the problem of recovering either a low rank matrix or a sparse vector from observations of linear combinations of the vector or matrix elements. Recent methods replace the non-convex regularization with l1 or nuclear norm relaxations. It is well known that this approach recovers near optimal solutions if a so called restricted isometry property (RIP) holds. On the other hand it also has a shrinking bias which can degrade the solution. In this paper we study an alternative non-convex regularization term that does not suffer from this bias. Our main theoretical results show that if a RIP holds then the stationary points are often well separated, in the sense that their differences must be of high cardinality/rank. Thus, with a suitable initial solution the approach is unlikely to fall into a bad local minimum. Our numerical tests show that the approach is likely to converge to a better solution than standard l1/nuclear-norm relaxation even when starting from trivial initializations. In many cases our results can also be used to verify global optimality of our method.

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author = {Olsson, Carl and Carlsson, Marcus and Andersson, Fredrik and Larsson, Viktor},
title = {Non-Convex Rank/Sparsity Regularization and Local Minima},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {Oct},
year = {2017}