Estimating number of components in Gaussian mixture model using combination of greedy and merging algorithm

0484891 - ÚI 2018 GB eng J - Journal Article
Štepánová, K. - Vavrečka, Michal
Estimating number of components in Gaussian mixture model using combination of greedy and merging algorithm.
Pattern Analysis and Applications. Roč. 21, č. 1 (2018), s. 181-192. ISSN 1433-7541. E-ISSN 1433-755X
Keywords : Clustering * EM algorithm * Gaussian mixture model * Mixture model * Number of clusters
Impact factor: 1.410, year: 2018

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.
Permanent Link: http://hdl.handle.net/11104/0280014