Efficient methods for grouping vectors into low-rank clusters

DOI10.1016/j.jcp.2011.03.048zbMath1220.65053OpenAlexW2000214323MaRDI QIDQ550984

Aaditya V. Rangan

Publication date: 13 July 2011

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

Full work available at URL: https://doi.org/10.1016/j.jcp.2011.03.048

zbMATH Keywords

algorithmsnumerical examplessubdivision schemequicksortdata-analysishierarchical factorizationlow-rank clustersmatrix-compressionprincipal-component-analysisvector sorting

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Searching and sorting (68P10)

Related Items

A simple filter for detecting low-rank submatrices ⋮ Detecting low-rank clusters via random sampling

Uses Software

• UCI-ml

Cites Work

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• 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
• Randomized algorithms for the low-rank approximation of matrices
• Efficient Algorithms for Computing a Strong Rank-Revealing QR Factorization
• On the Compression of Low Rank Matrices
• An Algorithm for the Machine Calculation of Complex Fourier Series