\(e\)PCA: high dimensional exponential family PCA

DOI10.1214/18-AOAS1146zbMath1411.62376arXiv1611.05550OpenAlexW2963106396WikidataQ107063155 ScholiaQ107063155MaRDI QIDQ1728639

Lydia T. Liu, Amit Singer, Edgar Dobriban

Publication date: 25 February 2019

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1611.05550

zbMATH Keywords

random matrix theorydenoisingshrinkageXFEL imaging

Mathematics Subject Classification ID

Image analysis in multivariate analysis (62H35) Applications of statistics to physics (62P35)

Related Items (13)

Heteroskedastic PCA: algorithm, optimality, and applications ⋮ On the non-asymptotic concentration of heteroskedastic Wishart-type matrix ⋮ Biwhitening Reveals the Rank of a Count Matrix ⋮ A literature review of (Sparse) exponential family PCA ⋮ Poisson PCA: Poisson measurement error corrected PCA, with application to microbiome data ⋮ Percolate: an exponential family JIVE model to design DNA-based predictors of drug response ⋮ Imputation and low-rank estimation with missing not at random data ⋮ \(e\)PCA: high dimensional exponential family PCA ⋮ Variational inference for probabilistic Poisson PCA ⋮ Matrix Denoising for Weighted Loss Functions and Heterogeneous Signals ⋮ Low-rank model with covariates for count data with missing values ⋮ ePCA ⋮ Low-rank matrix denoising for count data using unbiased Kullback-Leibler risk estimation

Uses Software

• DEseq
• SPECTRODE
• LowRankModels
• Pyglrm
• Condor
• OptShrink

Cites Work

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