 |
Université de la Rochelle |  |
|
Université de la Rochelle | |
| HAL: hal-00235514, version 1 |
| DOI: 10.1007/3-540-44522-6_90 |
| Detailed view |  Export this paper |
 |
| Statistical Pattern Recognition, Alicante : Espagne (2000) |
|
|
|
|
| Clustering with EM: complex models vs. robust estimation |
|
|
| Christophe Saint-Jean 1Carl Frélicot 1 |
|
|
| (2000-08-30) |
|
|
|
| Clustering multivariate data that are contaminated by noise is a complex issue, particularly in the framework of mixture model estimation because noisy data can significantly affect the parameters estimates. This paper addresses this problem with respect to likelihood maximization using the Expectation-Maximization algorithm. Two different approaches are compared. The first one consists in defining mixture models that take into account noise. The second one is based of robust estimation of the model parameters in the maximization step of EM. Both have been tested separately, then jointly. Finally, a hybrid model is proposed. Results on artificial data are given and discussed. |
|
|
|
|
|
|
|
|
|
|
|
| 1: | Laboratoire Informatique, Image et Interaction (L3I) |
|
|
|
Université de La Rochelle : EA2118 |
|
|
|
|
|
|
|
|
|
| Subject | : | Computer Science/Signal and Image Processing Engineering Sciences/Signal and Image processing |
|
|
| Clustering – Expectation-Maximization – Robustness – M-estimation |
|
|
|
| Attached file list to this document: | |||||
|
|||||
|
|
|
| hal-00235514, version 1 | |
| http://hal.archives-ouvertes.fr/hal-00235514 | |
| oai:hal.archives-ouvertes.fr:hal-00235514 | |
| From: Christophe Saint-Jean | |
| Submitted on: Monday, 4 February 2008 10:43:38 | |
| Updated on: Monday, 4 February 2008 10:47:21 | |
