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Estimating number of components in Gaussian mixture model using combination of greedy and merging algorithm
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SYSNO ASEP 0484891 Document Type J - Journal Article R&D Document Type The record was not marked in the RIV Subsidiary J Ostatní články Title Estimating number of components in Gaussian mixture model using combination of greedy and merging algorithm Author(s) Štepánová, K. (CZ)
Vavrečka, Michal (UIVT-O)Source Title Pattern Analysis and Applications - ISSN 1433-7541
Roč. 21, č. 1 (2018), s. 181-192Number of pages 12 s. Language eng - English Country GB - United Kingdom Keywords Clustering ; EM algorithm ; Gaussian mixture model ; Mixture model ; Number of clusters DOI 10.1007/s10044-016-0576-5 Annotation The brain must deal with a massive flow of sensory information without receiving any prior information. Therefore, when creating cognitive models, it is important to acquire as much information as possible from the data itself. Moreover, the brain has to deal with an unknown number of components (concepts) contained in a dataset without any prior knowledge. Most of the algorithms in use today are not able to effectively copy this strategy. We propose a novel approach based on neural modelling fields theory (NMF) to overcome this problem. The algorithm combines NMF and greedy Gaussian mixture models. The novelty lies in the combination of information criterion with the merging algorithm. The performance of the algorithm was compared with other well-known algorithms and tested both on artificial and real-world datasets. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2018
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