On clustering histograms with \(k\)-means by using mixed \(\alpha\)-divergences

DOI10.3390/e16063273zbMath1341.62186OpenAlexW2034182200MaRDI QIDQ296474

Richard Nock, Frank Nielsen, Shun-ichi Amari

Publication date: 15 June 2016

Published in: Entropy (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.3390/e16063273

zbMATH Keywords

centroid\(k\)-means clustering\(\alpha\)-divergence\(k\)-means seedingbag-of-\(X\)Jeffreys divergence

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Statistical aspects of information-theoretic topics (62B10)

Related Items (2)

Quasi-arithmetic centers, quasi-arithmetic mixtures, and the Jensen-Shannon \(\nabla \)-divergences ⋮ Clustering in Hilbert’s Projective Geometry: The Case Studies of the Probability Simplex and the Elliptope of Correlation Matrices

Cites Work

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• Entropic means
• Beiträge zur Störungstheorie der Spektralzerlegung
• On the invariance equation for Heinz means
• Real values of the W -function
• The α-EM algorithm: surrogate likelihood maximization using α-logarithmic information measures
• Least squares quantization in PCM
• $\alpha$-Divergence Is Unique, Belonging to Both $f$-Divergence and Bregman Divergence Classes
• Sided and Symmetrized Bregman Centroids
• The Burbea-Rao and Bhattacharyya Centroids
• Integration of Stochastic Models by Minimizing α-Divergence
• Markov Processes and the H-Theorem
• A Measure of Asymptotic Efficiency for Tests of a Hypothesis Based on the sum of Observations

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