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AbstractWe introduced two novel classes of Hölder divergences and Hölder pseudo-divergences that are both invariant to rescaling, and that both encapsulate the Cauchy-Schwarz divergence and the skew Bhattacharyya divergences. We review the elementary concepts of those parametric divergences, and perform a clustering analysis on two synthetic datasets. It is shown experimentally that the symmetrized Hölder divergences consistently outperform significantly the Cauchy-Schwarz divergence in clustering tasks.
CitationNielsen F, Sun K, Marchand-Maillet S (2017) k-Means Clustering with Hölder Divergences. Geometric Science of Information: 856–863. Available: http://dx.doi.org/10.1007/978-3-319-68445-1_98.
PublisherSpringer International Publishing
Conference/Event nameInternational Conference on Geometric Science of Information