Detecting low-rank clusters via random sampling

DOI10.1016/j.jcp.2011.09.008zbMath1243.65020OpenAlexW2017555472MaRDI QIDQ425614

Aaditya V. Rangan

Publication date: 8 June 2012

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jcp.2011.09.008

zbMATH Keywords

algorithmnumerical examplesdata analysislow-rank clusterrandom rotation projection

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items (1)

A simple filter for detecting low-rank submatrices

Cites Work

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• Efficient methods for grouping vectors into low-rank clusters
• A randomized algorithm for the decomposition of matrices
• A fast randomized algorithm for the approximation of matrices
• The Mailman algorithm: a note on matrix-vector multiplication
• A randomized approximate nearest neighbors algorithm
• Approximate nearest neighbors and the fast Johnson-Lindenstrauss transform
• Randomized algorithms for the low-rank approximation of matrices

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