Approximation of Unknown Multivariate Probability Distributions by Using Mixtures of Product Components: A Tutorial

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F17%3A00475182" target="_blank" >RIV/67985556:_____/17:00475182 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1142/S0218001417500288" target="_blank" >http://dx.doi.org/10.1142/S0218001417500288</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1142/S0218001417500288" target="_blank" >10.1142/S0218001417500288</a>

Result language

angličtina

Original language name

Approximation of Unknown Multivariate Probability Distributions by Using Mixtures of Product Components: A Tutorial

Original language description

In literature the references to EM estimation of product mixtures are not very frequent. The simplifying assumption of product components, e.g. diagonal covariance matrices in case of Gaussian mixtures, is usually considered only as a compromise because of some computational constraints or limited data set. We have found that the product mixtures are rarely used intentionally as a preferable approximating tool. Probably, most practitioners do not „trust“ the product components because of their formal similarity to „naive Bayes models“. Another reason could be an unrecognized numerical instability of EM algorithm in multidimensional spaces. In this paper we recall that the product mixture model does not imply the assumption of independence of variables. It is even not restrictive if the number of components is large enough. In addition, the product components increase numerical stability of the standard EM algorithm, simplify the EM iterations and have some other important advantages. We discuss and explain the implementation details of EM algorithm and summarize our experience in estimating product mixtures. Finally we illustrate the wide applicability of product mixtures in pattern recognition and in other fields.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GA17-18407S" target="_blank" >GA17-18407S: Perceptually Optimized Measurement of Material Appearance</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2017

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

International Journal of Pattern Recognition and Artificial Intelligence

ISSN

0218-0014

e-ISSN

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Volume of the periodical

31

Issue of the periodical within the volume

9

Country of publishing house

GB - UNITED KINGDOM

Number of pages

37

Pages from-to

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UT code for WoS article

000402745500001

EID of the result in the Scopus database

2-s2.0-85016474470