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- W3115233572 abstract "When the dimension of data is comparable to or larger than the number of data samples, Principal Components Analysis (PCA) may exhibit problematic high-dimensional noise. In this work, we propose an Empirical Bayes PCA method that reduces this noise by estimating a joint prior distribution for the principal components. EB-PCA is based on the classical Kiefer-Wolfowitz nonparametric MLE for empirical Bayes estimation, distributional results derived from random matrix theory for the sample PCs, and iterative refinement using an Approximate Message Passing (AMP) algorithm. In theoretical spiked models, EB-PCA achieves Bayes-optimal estimation accuracy in the same settings as an oracle Bayes AMP procedure that knows the true priors. Empirically, EB-PCA significantly improves over PCA when there is strong prior structure, both in simulation and on quantitative benchmarks constructed from the 1000 Genomes Project and the International HapMap Project. An illustration is presented for analysis of gene expression data obtained by single-cell RNA-seq." @default.
- W3115233572 created "2021-01-05" @default.
- W3115233572 creator A5033747001 @default.
- W3115233572 creator A5045324437 @default.
- W3115233572 creator A5047187143 @default.
- W3115233572 date "2020-12-21" @default.
- W3115233572 modified "2023-09-26" @default.
- W3115233572 title "Empirical Bayes PCA in high dimensions" @default.
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