Supervised and unsupervised classification using mixture models - Archive ouverte HAL Access content directly
Preprints, Working Papers, ... Year : 2015

Supervised and unsupervised classification using mixture models

Abstract

This chapter is dedicated to model-based supervised and unsuper-vised classification. Probability distributions are defined over possible labels as well as over the observations given the labels. To this end, the basic tools are the mixture models. This methodology yields a posterior distribution over the labels given the observations which allows to quantify the uncertainty of the classification. The role of Gaussian mixture models is emphasized leading to Linear Discriminant Analysis and Quadratic Discriminant Analysis methods. Some links with Fisher Discriminant Analysis and logistic regression are also established. The Expectation-Maximization algorithm is introduced and compared to the K-means clustering method. The methods are illustrated both on simulated datasets as well as on real datasets using the R software.
Fichier principal
Vignette du fichier
chapClassification-hal.pdf (375.59 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01241818 , version 1 (11-12-2015)

Identifiers

  • HAL Id : hal-01241818 , version 1

Cite

Stéphane Girard, Jerome Saracco. Supervised and unsupervised classification using mixture models. 2015. ⟨hal-01241818⟩
389 View
1948 Download

Share

Gmail Facebook X LinkedIn More