A STATISTICAL FRAMEWORK FOR POSITIVE DATA CLUSTERING WITH FEATURE SELECTION : APPLICATION TO OBJECT DETECTION - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2013

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.
Fichier principal
Vignette du fichier
Eusipco2013.pdf (3.26 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-00908379 , version 1 (22-11-2013)

Identifiers

  • HAL Id : hal-00908379 , version 1

Cite

Mohamed Al Mashrgy, Nizar Bouguila, Khalid Daoudi. A STATISTICAL FRAMEWORK FOR POSITIVE DATA CLUSTERING WITH FEATURE SELECTION : APPLICATION TO OBJECT DETECTION. Eusipco, Sep 2013, Marrakech, Morocco. ⟨hal-00908379⟩
350 View
211 Download

Share

Gmail Facebook X LinkedIn More