Low-rank matrix denoising for count data using unbiased Kullback-Leibler risk estimation

DOI10.1016/j.csda.2022.107423OpenAlexW3004372255MaRDI QIDQ2076123

Jérémie Bigot, Charles-Alban Deledalle

Publication date: 18 February 2022

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2001.10391

zbMATH Keywords

count dataPoisson distributionmultinomial distributionKullback-Leibler risknuclear norm penalizationlow-rank matrix denoisinggeneralized Stein's unbiased risk estimatemetagenomics dataoptimal shrinkage rulesurvey study

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items

Biwhitening Reveals the Rank of a Count Matrix

Uses Software

• WordNet
• LowRankModels
• Pyglrm
• ePCA
• OptShrink

Cites Work

• Unnamed Item
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