Title :
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
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;
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
DOI :
10.1109/ISCAS.2006.1693317
