2023
DOI: 10.48550/arxiv.2301.05331
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Detection problems in the spiked matrix models

Abstract: We study the statistical decision process of detecting the low-rank signal from various signal-plusnoise type data matrices, known as the spiked random matrix models. We first show that the principal component analysis can be improved by entrywise pre-transforming the data matrix if the noise is non-Gaussian, generalizing the known results for the spiked random matrix models with rank-1 signals. As an intermediate step, we find out sharp phase transition thresholds for the extreme eigenvalues of spiked random … Show more

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