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 language | English |
|---|---|
| Title of host publication | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings |
| Pages | 2092-2095 |
| Number of pages | 4 |
| DOIs | |
| Publication status | Published - 18 Aug 2011 |
| Event | 2011 (36th) IEEE International Conference on Acoustics, Speech, and Signal Processing - Prague, Czech Republic Duration: 22 May 2011 → 27 May 2011 |
Conference
| Conference | 2011 (36th) IEEE International Conference on Acoustics, Speech, and Signal Processing |
|---|---|
| Abbreviated title | ICASSP '11 |
| Country/Territory | Czech Republic |
| City | Prague |
| Period | 22/05/11 → 27/05/11 |
Keywords
- Clustering
- kernel methods
- sequences
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