List Decodable Subspace Recovery
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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 α 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(1/α) 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.
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