Fast rates for empirical vector quantization

DOI10.1214/13-EJS822zbMath1349.62038arXiv1201.6052MaRDI QIDQ351685

Clément Levrard

Publication date: 9 July 2013

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1201.6052

zbMATH Keywords

clusteringquantizationlocalizationfast rates

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Approximations to statistical distributions (nonasymptotic) (62E17)

Related Items (9)

Compressive statistical learning with random feature moments ⋮ Statistical learning guarantees for compressive clustering and compressive mixture modeling ⋮ Empirical risk minimization for heavy-tailed losses ⋮ Also for \(k\)-means: more data does not imply better performance ⋮ Dimensionality-Dependent Generalization Bounds for k-Dimensional Coding Schemes ⋮ Bandwidth selection in kernel empirical risk minimization via the gradient ⋮ Robust \(k\)-means clustering for distributions with two moments ⋮ On strong consistency of kernel \(k\)-means: a Rademacher complexity approach ⋮ Nonasymptotic bounds for vector quantization in Hilbert spaces

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