Computational and Statistical Boundaries for Submatrix Localization in a Large Noisy

Abstract

We study in this paper computational and statistical boundaries for submatrix localization. Given one observation of (one or multiple non-overlapping) signal submatrix (of magnitude λ and size km×kn) embedded in a large noise matrix (of size m × n), the goal is to optimal identify the support of the signal submatrix computationally and statistically. Two… (More)

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