Assessment of probability density estimation methods: Parzen window and finite Gaussian mixtures

Author

Archambeau, C. ; Valle, M. ; Assenza, A. ; Verleysen, M.

Author_Institution

DICE, Univ. Catholique de Louvain, Louvain-la-Neuve

fYear

2006

fDate

21-24 May 2006

Abstract

Probability density function (PDF) estimation is a very critical task in many applications of data analysis. For example in the Bayesian framework decisions are taken according to Bayes´ rule, which directly involves the evaluation of the PDF. Many methods are available to this aim, but there is no consensus in the literature about which to use, nor about the pros and cons of each of them. In this paper, we present a thorough and extensive experimental comparison between two of the most popular methods: Parzen window and finite Gaussian mixture. Extended experimental results and application development guidelines are reported

Keywords

Bayes methods; Gaussian processes; estimation theory; Bayesian framework decisions; Parzen window; finite Gaussian mixtures; probability density estimation methods; probability density function estimation; Bayesian methods; Data analysis; Data mining; Density functional theory; Guidelines; Multilayer perceptrons; Principal component analysis; Probability density function; Random variables; Statistics;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 2006. ISCAS 2006. Proceedings. 2006 IEEE International Symposium on

Conference_Location

Island of Kos

Print_ISBN

0-7803-9389-9

Type

conf

DOI

10.1109/ISCAS.2006.1693317

Filename

1693317