Type
Conference PaperDate
2017-10-24Online Publication Date
2017-10-24Print Publication Date
2017Permanent link to this record
http://hdl.handle.net/10754/626144
Metadata
Show full item recordAbstract
We 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.Citation
Nielsen 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.Publisher
Springer NatureConference/Event name
International Conference on Geometric Science of InformationAdditional Links
https://link.springer.com/chapter/10.1007%2F978-3-319-68445-1_98ae974a485f413a2113503eed53cd6c53
10.1007/978-3-319-68445-1_98