OptShrink
swMATH ID:  33657 
Software Authors:  Nadakuditi, Raj Rao 
Description:  OptShrink: an algorithm for improved lowrank signal matrix denoising by optimal, datadriven singular value shrinkage. OptShrink is a simple, completely datadriven algorithm for denoising a lowrank signal matrix buried in noise. It takes as its input the signalplusnoise matrix, an estimate of the signal matrix rank and returns as an output the improved signal matrix estimate. It computes this estimate by shrinking the singular values corresponding to the Truncated SVD (TSVD) in the correct manner as given by random matrix theory. It can be used in the missing data setting and for a large class of noise models for which the i.i.d. Gaussian setting is a special case. There are no tuning parameters involved so it can be used in a blackbox manner wherever improving lowrank matrix estimation is desirable. The algorithm outperforms the truncated SVD (TSVD) significantly in the low to moderate SNR regime and will never do worse than the TSVD. The theory also explains why it will always do better than singular value thresholding. 
Homepage:  https://web.eecs.umich.edu/~rajnrao/optshrink/ 
Dependencies:  Matlab 
Related Software:  SPECTRODE; ePCA; Pyglrm; LowRankModels; QuEST; softImpute; RMTool; Mcmcpack; WordNet; ElemStatLearn; nFactors; PROPACK; DLMF; odmd; BSSasymp; JADE; BRENT; leapp; Condor; DEseq 
Cited in:  21 Publications 
Standard Articles
1 Publication describing the Software, including 1 Publication in zbMATH  Year 

OptShrink: an algorithm for improved lowrank signal matrix denoising by optimal, datadriven singular value shrinkage. Zbl 1360.62399 Nadakuditi, Raj Rao 
2014

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Cited by 24 Authors
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