List Decodable Subspace Recovery

Prasad Raghavendra, Morris Yau
; Proceedings of Thirty Third Conference on Learning Theory, PMLR 125:3206-3226, 2020.

Abstract

Learning from data in the presence of outliers is a fundamental problem in statistics. In this work, we study robust statistics in the presence of overwhelming outliers for the fundamental problem of subspace recovery. Given a dataset where an $\alpha$ fraction (less than half) of the data is distributed uniformly in an unknown $k$ dimensional subspace in $d$ dimensions, and with no additional assumptions on the remaining data, the goal is to recover a succinct list of $O(\frac{1}{\alpha})$ subspaces one of which is nontrivially correlated with the planted subspace. We provide the first polynomial time algorithm for the ’list decodable subspace recovery’ problem, and subsume it under a more general framework of list decoding over distributions that are "certifiably resilient" capturing state of the art results for list decodable mean estimation and regression.

Cite this Paper


BibTeX
@InProceedings{pmlr-v125-raghavendra20a, title = {List Decodable Subspace Recovery}, author = {Raghavendra, Prasad and Yau, Morris}, pages = {3206--3226}, year = {2020}, editor = {Jacob Abernethy and Shivani Agarwal}, volume = {125}, series = {Proceedings of Machine Learning Research}, address = {}, month = {09--12 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v125/raghavendra20a/raghavendra20a.pdf}, url = {http://proceedings.mlr.press/v125/raghavendra20a.html}, abstract = { Learning from data in the presence of outliers is a fundamental problem in statistics. In this work, we study robust statistics in the presence of overwhelming outliers for the fundamental problem of subspace recovery. Given a dataset where an $\alpha$ fraction (less than half) of the data is distributed uniformly in an unknown $k$ dimensional subspace in $d$ dimensions, and with no additional assumptions on the remaining data, the goal is to recover a succinct list of $O(\frac{1}{\alpha})$ subspaces one of which is nontrivially correlated with the planted subspace. We provide the first polynomial time algorithm for the ’list decodable subspace recovery’ problem, and subsume it under a more general framework of list decoding over distributions that are "certifiably resilient" capturing state of the art results for list decodable mean estimation and regression.} }
Endnote
%0 Conference Paper %T List Decodable Subspace Recovery %A Prasad Raghavendra %A Morris Yau %B Proceedings of Thirty Third Conference on Learning Theory %C Proceedings of Machine Learning Research %D 2020 %E Jacob Abernethy %E Shivani Agarwal %F pmlr-v125-raghavendra20a %I PMLR %J Proceedings of Machine Learning Research %P 3206--3226 %U http://proceedings.mlr.press %V 125 %W PMLR %X Learning from data in the presence of outliers is a fundamental problem in statistics. In this work, we study robust statistics in the presence of overwhelming outliers for the fundamental problem of subspace recovery. Given a dataset where an $\alpha$ fraction (less than half) of the data is distributed uniformly in an unknown $k$ dimensional subspace in $d$ dimensions, and with no additional assumptions on the remaining data, the goal is to recover a succinct list of $O(\frac{1}{\alpha})$ subspaces one of which is nontrivially correlated with the planted subspace. We provide the first polynomial time algorithm for the ’list decodable subspace recovery’ problem, and subsume it under a more general framework of list decoding over distributions that are "certifiably resilient" capturing state of the art results for list decodable mean estimation and regression.
APA
Raghavendra, P. & Yau, M.. (2020). List Decodable Subspace Recovery. Proceedings of Thirty Third Conference on Learning Theory, in PMLR 125:3206-3226

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