Low-rank regularization and solution uniqueness in over-parameterized matrix sensing

Kelly Geyer, Anastasios Kyrillidis, Amir Kalev
Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, PMLR 108:930-940, 2020.

Abstract

We consider the question whether algorithmic choices in over-parameterized linear matrix factorization introduce implicit low-rank regularization.We focus on the noiseless matrix sensing scenario over low-rank positive semi-definite (PSD) matrices over the reals, with a sensing mechanism that satisfies restricted isometry properties.Surprisingly, it was recently argued that for recovery of PSD matrices, gradient descent over a squared, \textit{full-rank} factorized space introduces implicit low-rank regularization.Thus, a clever choice of the recovery algorithm avoids the need for explicit low-rank regularization. In this contribution, we prove that in fact, under certain conditions, the PSD constraint by itself is sufficient to lead to a unique low-rank matrix recovery, without explicit or implicit regularization.Therefore, under these conditions, the set of PSD matrices that are consistent with the observed data, is a singleton, regardless of the algorithm used. Our numerical study indicates that this result is general and extends to cases beyond the those covered by the proof.

Cite this Paper


BibTeX
@InProceedings{pmlr-v108-geyer20a, title = {Low-rank regularization and solution uniqueness in over-parameterized matrix sensing}, author = {Geyer, Kelly and Kyrillidis, Anastasios and Kalev, Amir}, booktitle = {Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics}, pages = {930--940}, year = {2020}, editor = {Silvia Chiappa and Roberto Calandra}, volume = {108}, series = {Proceedings of Machine Learning Research}, month = {26--28 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v108/geyer20a/geyer20a.pdf}, url = { http://proceedings.mlr.press/v108/geyer20a.html }, abstract = {We consider the question whether algorithmic choices in over-parameterized linear matrix factorization introduce implicit low-rank regularization.We focus on the noiseless matrix sensing scenario over low-rank positive semi-definite (PSD) matrices over the reals, with a sensing mechanism that satisfies restricted isometry properties.Surprisingly, it was recently argued that for recovery of PSD matrices, gradient descent over a squared, \textit{full-rank} factorized space introduces implicit low-rank regularization.Thus, a clever choice of the recovery algorithm avoids the need for explicit low-rank regularization. In this contribution, we prove that in fact, under certain conditions, the PSD constraint by itself is sufficient to lead to a unique low-rank matrix recovery, without explicit or implicit regularization.Therefore, under these conditions, the set of PSD matrices that are consistent with the observed data, is a singleton, regardless of the algorithm used. Our numerical study indicates that this result is general and extends to cases beyond the those covered by the proof.} }
Endnote
%0 Conference Paper %T Low-rank regularization and solution uniqueness in over-parameterized matrix sensing %A Kelly Geyer %A Anastasios Kyrillidis %A Amir Kalev %B Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2020 %E Silvia Chiappa %E Roberto Calandra %F pmlr-v108-geyer20a %I PMLR %P 930--940 %U http://proceedings.mlr.press/v108/geyer20a.html %V 108 %X We consider the question whether algorithmic choices in over-parameterized linear matrix factorization introduce implicit low-rank regularization.We focus on the noiseless matrix sensing scenario over low-rank positive semi-definite (PSD) matrices over the reals, with a sensing mechanism that satisfies restricted isometry properties.Surprisingly, it was recently argued that for recovery of PSD matrices, gradient descent over a squared, \textit{full-rank} factorized space introduces implicit low-rank regularization.Thus, a clever choice of the recovery algorithm avoids the need for explicit low-rank regularization. In this contribution, we prove that in fact, under certain conditions, the PSD constraint by itself is sufficient to lead to a unique low-rank matrix recovery, without explicit or implicit regularization.Therefore, under these conditions, the set of PSD matrices that are consistent with the observed data, is a singleton, regardless of the algorithm used. Our numerical study indicates that this result is general and extends to cases beyond the those covered by the proof.
APA
Geyer, K., Kyrillidis, A. & Kalev, A.. (2020). Low-rank regularization and solution uniqueness in over-parameterized matrix sensing. Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 108:930-940 Available from http://proceedings.mlr.press/v108/geyer20a.html .

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