Paper Info
Title
A novel split-and-merge algorithm for hierarchical clustering of Gaussian mixture models
Abstract
The paper presents a novel split-and-merge algorithm for hierarchical clustering of Gaussian mixture models, which tends to improve on the local optimal solution determined by the initial constellation. It is initialized by local optimal parameters obtained by using a baseline approach similar to k-means, and it tends to approach more closely to the global optimum of the target clustering function, by iteratively splitting and merging the clusters of Gaussian components obtained as the output of the baseline algorithm. The algorithm is further improved by introducing model selection in order to obtain the best possible trade-off between recognition accuracy and computational load in a Gaussian selection task applied within an actual recognition system. The proposed method is tested both on artificial data and in the framework of Gaussian selection performed within a real continuous speech recognition system, and in both cases an improvement over the baseline method has been observed.
Year
DOI
Venue
2012
10.1007/s10489-011-0333-9
Appl. Intell.
Keywords
Field
DocType
Gaussian mixtures,Split-and-merge operation,Hierarchical clustering,Continuous speech recognition
Hierarchical clustering,Canopy clustering algorithm,CURE data clustering algorithm,Pattern recognition,Computer science,Model selection,Determining the number of clusters in a data set,Gaussian,Artificial intelligence,Cluster analysis,Mixture model,Machine learning
Journal
Volume
Issue
ISSN
37
3
0924-669X
Citations 
PageRank 
References 
6
0.63
22
Authors
7
Name
Order
Citations
PageRank
Branislav M. Popovic19617.13
Marko Janev2454.79
Darko Pekar3378.28
Nikša Jakovljević4284.11
Milan Gnjatović5486.85
Milan Secujski6226.51
Vlado Delić75212.26