Computational and statistical boundaries for submatrix localization in a large noisy matrix (Q2403425)
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scientific article
| Language | Label | Description | Also known as |
|---|---|---|---|
| English | Computational and statistical boundaries for submatrix localization in a large noisy matrix |
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Computational and statistical boundaries for submatrix localization in a large noisy matrix (English)
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8 September 2017
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The paper deals with the analysis of the interplay between computational efficiency and statistical accuracy in submatrix localization based on a noisy observation of a large matrix. In this respect the authors propose and analyse a new linear time spectral algorithm that identifies the submatrix. They show that the localization problem requires larger signal-to-noise ratio. They also consider a new analysis of a known method that is based on a convex relaxation for comparison with the spectral method.
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computational boundary
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computational complexity
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detection
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planted clique
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lower bounds
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minimax
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signal-to-noise ratio
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statistical boundary
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submatrix localization
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