How to Reduce Dimension With PCA and Random Projections?

DOI10.1109/TIT.2021.3112821zbMath1489.94047arXiv2005.00511OpenAlexW3201018963MaRDI QIDQ5032651

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Publication date: 17 February 2022

Published in: IEEE Transactions on Information Theory (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2005.00511

zbMATH Keywords

principal component analysis (PCA)

Mathematics Subject Classification ID

Informational aspects of data analysis and big data (94A16)

Related Items (5)

Dimensionality Reduction, Regularization, and Generalization in Overparameterized Regressions ⋮ Sample canonical correlation coefficients of high-dimensional random vectors with finite rank correlations ⋮ Local laws for multiplication of random matrices ⋮ Spiked multiplicative random matrices and principal components ⋮ Limiting distribution of the sample canonical correlation coefficients of high-dimensional random vectors

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