Spontaneous Clustering via Minimum Gamma-Divergence

DOI10.1162/NECO_a_00547zbMath1418.62255arXiv1304.7867OpenAlexW2081663123WikidataQ45104984 ScholiaQ45104984MaRDI QIDQ5378327

Akifumi Notsu, Osamu Komori, Shinto Eguchi

Publication date: 12 June 2019

Published in: Neural Computation (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1304.7867

zbMATH Keywords

minimum gamma-divergencespontaneous clustering

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to physics (62P35)

Related Items (4)

On the weak convergence and central limit theorem of blurring and nonblurring processes with application to robust location estimation ⋮ Robust Clustering Method in the Presence of Scattered Observations ⋮ Cramér-Rao lower bounds arising from generalized Csiszár divergences ⋮ Monte Carlo Information-Geometric Structures

Uses Software

• ElemStatLearn
• PRMLT

Cites Work

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• Detection of heterogeneous structures on the Gaussian copula model using projective power entropy
• Projective Clustering Using Neural Networks with Adaptive Delay and Signal Transmission Loss
• The Concave-Convex Procedure

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