Learning-based low-rank approximations
Name
NeurIPS-2019-learning-based-low-rank-approximations-Paper.pdf
Description
Published version
Size
1.28 MB
Format
Adobe PDF
Checksum (MD5)
5c6d1bfcdd160bf88fd7a3288daf68d0
Author(s)
Indyk, Piotr
Date Issued
December 2019
Journal
Advances in Neural Information Processing Systems
Publisher
Morgan Kaufmann Publishers
Citation
Indyk, Piotr et al. “Learning-based low-rank approximations.” Advances in Neural Information Processing Systems, 32 (December 2019) © 2019 The Author(s)
Version
Final published version
Abstract
We introduce a “learning-based” algorithm for the low-rank decomposition problem: given an n × d matrix A, and a parameter k, compute a rank-k matrix A0 that minimizes the approximation loss ||A - A0||F. The algorithm uses a training set of input matrices in order to optimize its performance. Specifically, some of the most efficient approximate algorithms for computing low-rank approximations proceed by computing a projection SA, where S is a sparse random m × n “sketching matrix”, and then performing the singular value decomposition of SA. We show how to replace the random matrix S with a “learned” matrix of the same sparsity to reduce the error. Our experiments show that, for multiple types of data sets, a learned sketch matrix can substantially reduce the approximation loss compared to a random matrix S, sometimes by one order of magnitude. We also study mixed matrices where only some of the rows are trained and the remaining ones are random, and show that matrices still offer improved performance while retaining worst-case guarantees. Finally, to understand the theoretical aspects of our approach, we study the special case of m = 1. In particular, we give an approximation algorithm for minimizing the empirical loss, with approximation factor depending on the stable rank of matrices in the training set. We also show generalization bounds for the sketch matrix learning problem.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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