Abstract
In kernel density estimation methods, an approximation of the data probability density function is achieved by locating a kernel junction at each data location. The smoothness of the functional approximation and the modelling ability are controlled by the kernel bandwidth. In this paper we propose a Bayesian estimation method for finding the kernel bandwidth. The distribution corresponding to the bandwidth is estimated from distributions characterizing the second order statistics estimates calculated from local neighbourhoods. The proposed bandwidth estimation method is applied in three different kernel density estimation based approaches: scale space, mean shift and quantum clustering. The third method is a novel pattern recognition approach using the principles of quantum mechanics.
| Original language | English |
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| Title of host publication | 19TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOLS 1-6 |
| Place of Publication | NEW YORK |
| Publisher | IEEE |
| Pages | 3354-3357 |
| Number of pages | 4 |
| ISBN (Print) | 978-1-4244-2174-9 |
| DOIs | |
| Publication status | Published - 2008 |
| Event | 19th International Conference on Pattern Recognition (ICPR 2008) - Tampa Duration: 8 Dec 2008 → 11 Dec 2008 |
Conference
| Conference | 19th International Conference on Pattern Recognition (ICPR 2008) |
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| City | Tampa |
| Period | 8/12/08 → 11/12/08 |
Keywords
- SELECTION