Estimating Rank-One Spikes from Heavy-Tailed Noise via Self-Avoiding Walks

NIPS 2020, 2020.

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First we show that for many long tail distributions, Principal component analysis algorithm can be saved by such truncation.

Abstract:

We study symmetric spiked matrix models with respect to a general class of noise distributions. Given a rank-1 deformation of a random noise matrix, whose entries are independently distributed with zero mean and unit variance, the goal is to estimate the rank-1 part. For the case of Gaussian noise, the top eigenvector of the given matri...More

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