Quantization and clustering with Bregman divergences

DOI10.1016/j.jmva.2010.05.008zbMath1201.62080OpenAlexW2079561525MaRDI QIDQ990903

Aurélie Fischer

Publication date: 1 September 2010

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmva.2010.05.008

zbMATH Keywords

Banach spacesquantizationrates of convergence\(k\)-means clusteringBregman divergences

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Inequalities; stochastic orderings (60E15) Applications of functional analysis in probability theory and statistics (46N30) Limit theorems in probability theory (60F99)

Related Items (10)

On a time dependent divergence measure between two residual lifetime distributions ⋮ Robust Bregman clustering ⋮ Fast rates for empirical vector quantization ⋮ Prediction in Riemannian metrics derived from divergence functions ⋮ Statistical learning guarantees for compressive clustering and compressive mixture modeling ⋮ Conditional quantile estimation through optimal quantization ⋮ Classification into Kullback-Leibler balls in exponential families ⋮ Clustering of measures via mean measure quantization ⋮ Robust \(k\)-means clustering for distributions with two moments ⋮ Nonasymptotic bounds for vector quantization in Hilbert spaces

Uses Software

• fda (R)

Cites Work

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