Sphere packing for clustering sets of vectors in feature space

Darío García-García*, Raúl Santos-Rodríguez

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

2 Citations (Scopus)

Abstract

We propose a method for clustering sets of vectors by packing spheres learnt to represent the support of the different sets. The algorithm can work efficiently in a kernel-induced feature space by using the kernel trick. Experimental results on synthetic and real-world datasets show that the proposal is competitive with the state of the art.

Original languageEnglish
Title of host publicationICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Pages2092-2095
Number of pages4
DOIs
Publication statusPublished - 18 Aug 2011
Event2011 (36th) IEEE International Conference on Acoustics, Speech, and Signal Processing - Prague, Czech Republic
Duration: 22 May 201127 May 2011

Conference

Conference2011 (36th) IEEE International Conference on Acoustics, Speech, and Signal Processing
Abbreviated titleICASSP '11
CountryCzech Republic
CityPrague
Period22/05/1127/05/11

Keywords

  • Clustering
  • kernel methods
  • sequences

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