Abstract
Kernel density estimation (KDB) has been used in many computational intelligence and computer vision applications. In this paper we propose a Bayesian estimation method for finding the bat id in KDE applications. A Gamma density function is fitted to distributions of variances of K-nearest, neighbours data populations while uniform distribution priors are assumed for K. A maximum log-likelihood approach is used to estimate the parameters of the Gamma distribution when fitted to the local data variance. The proposed methodology is applied in three different KDE approaches: kernel sum, mean shift and quantum clustering. The third method relies on the Schrodinger partial differential equation and uses the analogy between the potential function that manifests around particles, as defined in quantum physics, and the probability density function corresponding to data. The proposed algorithm is applied to artificial data and to segment terrain images.
| Original language | English |
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| Title of host publication | ARTIFICIAL NEURAL NETWORKS - ICANN 2009, PT II |
| Editors | C Alippi, M Polycarppou, C Panayiotou, G Ellinas |
| Place of Publication | BERLIN |
| Publisher | Springer |
| Pages | 245-254 |
| Number of pages | 10 |
| Volume | 5769 LNCS |
| Edition | PART 2 |
| ISBN (Print) | 978-3-642-04276-8 |
| DOIs | |
| Publication status | Published - 2009 |
| Event | 19th International Conference on Artificial Neural Networks (ICANN 2009) - Limmassol Duration: 14 Sept 2009 → 17 Sept 2009 |
Conference
| Conference | 19th International Conference on Artificial Neural Networks (ICANN 2009) |
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| City | Limmassol |
| Period | 14/09/09 → 17/09/09 |
Keywords
- Kernel density estimation
- bandwidth
- quantum clustering
- DENSITY-ESTIMATION
- SELECTION
- REGRESSION