0-1 phase transitions in sparse spiked matrix estimation

Jean Barbier; Nicolas Macris

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arXiv · 1911.05030

0-1 phase transitions in sparse spiked matrix estimation

Jean Barbier·Nicolas Macris

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Abstract

We consider statistical models of estimation of a rank-one matrix (the spike) corrupted by an additive gaussian noise matrix in the sparse limit. In this limit the underlying hidden vector (that constructs the rank-one matrix) has a number of non-zero components that scales sub-linearly with the total dimension of the vector, and the signal strength tends to infinity at an appropriate speed. We prove explicit low-dimensional variational formulas for the asymptotic mutual information between the spike and the observed noisy matrix in suitable sparse limits. For Bernoulli and Bernoulli-Rademacher distributed vectors, and when the sparsity and signal strength satisfy an appropriate scaling relation, these formulas imply sharp 0-1 phase transitions for the asymptotic minimum mean-square-error. A similar phase transition was analyzed recently in the context of sparse high-dimensional linear regression (compressive sensing).

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cs.IT cond-mat.dis-nn cs.LG math.IT math.PR

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BibTeX RIS

Jean Barbier, Nicolas Macris. 2019-11-12. 0-1 phase transitions in sparse spiked matrix estimation. https://arxiv.org/abs/1911.05030

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