Name
Title | ||
|---|---|---|
A Statistical Framework For Positive Data Clustering With Feature Selection: Application To Object Detection |
Abstract | ||
|---|---|---|
In this paper, we concern ourselves with the problem of simultaneous positive data clustering and feature selection. We propose a statistical framework based on finite mixture models of generalized inverted Dirichlet (GID) distributions. The GID offers a more practical and flexible alternative to the inverted Dirichlet which has a very restrictive covariance structure. For learning the parameters of the resulting mixture, we propose an approach based on minimum message length (MML) criterion. We use synthetic data and real data generated from a challenging application that concerns objects detection to demonstrate the feasibility and advantages of the proposed method. |
Year | Venue | Keywords |
|---|---|---|
2013 | 2013 PROCEEDINGS OF THE 21ST EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO) | Positive data, feature selection, clustering, mixture models, GID, MML, object detection |
Field | DocType | Citations |
Data mining,Object detection,Minimum message length,Feature selection,Pattern recognition,Computer science,Synthetic data,Artificial intelligence,Dirichlet distribution,Cluster analysis,Mixture model,Covariance | Conference | 0 |
PageRank | References | Authors |
0.34 | 8 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohamed Al Mashrgy | 1 | 31 | 2.42 |
| Nizar Bouguila | 2 | 1539 | 146.09 |
| Khalid Daoudi | 3 | 145 | 23.68 |