Measure of similarity between gmms by embedding of the parameter space that preserves kl divergence

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27230%2F21%3A10247457" target="_blank" >RIV/61989100:27230/21:10247457 - isvavai.cz</a>

Result on the web

<a href="https://www.webofscience.com/wos/woscc/full-record/WOS:000650558100001" target="_blank" >https://www.webofscience.com/wos/woscc/full-record/WOS:000650558100001</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/math9090957" target="_blank" >10.3390/math9090957</a>

Result language

angličtina

Original language name

Measure of similarity between gmms by embedding of the parameter space that preserves kl divergence

Original language description

In this work, we deliver a novel measure of similarity between Gaussian mixture models (GMMs) by neighborhood preserving embedding (NPE) of the parameter space, that projects components of GMMs, which by our assumption lie close to lower dimensional manifold. By doing so, we obtain a transformation from the original high-dimensional parameter space, into a much lower-dimensional resulting parameter space. Therefore, resolving the distance between two GMMs is reduced to (taking the account of the corresponding weights) calculating the distance between sets of lower-dimensional Euclidean vectors. Much better trade-off between the recognition accuracy and the computational complexity is achieved in comparison to measures utilizing distances between Gaussian components evaluated in the original parameter space. The proposed measure is much more efficient in machine learning tasks that operate on large data sets, as in such tasks, the required number of overall Gaussian components is always large. Artificial, as well as real-world experiments are conducted, showing much better trade-off between recognition accuracy and computational complexity of the proposed measure, in comparison to all baseline measures of similarity between GMMs tested in this paper. (C) 2021 by the authors. Licensee MDPI, Basel, Switzerland.

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

10100 - Mathematics

Project

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Continuities

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2021

Confidentiality

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

Name of the periodical

Mathematics

ISSN

2227-7390

e-ISSN

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Volume of the periodical

9

Issue of the periodical within the volume

9

Country of publishing house

CH - SWITZERLAND

Number of pages

21

Pages from-to

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UT code for WoS article

000650558100001

EID of the result in the Scopus database

2-s2.0-85105307928