A novel split-and-merge algorithm for hierarchical clustering of Gaussian mixture models

Authors:
Branislav Popović;Marko Janev;Darko Pekar;Nikša Jakovljević;Milan Gnjatović;Milan Sečujski;Vlado Delić
Affiliations:
Faculty of Technical Sciences, University of Novi Sad, Novi Sad, Serbia;Mathematical Institute, Serbian Academy of Sciences and Arts, Belgrade, Serbia;Alfanum Speech Technologies, Novi Sad, Serbia;Faculty of Technical Sciences, University of Novi Sad, Novi Sad, Serbia;Faculty of Technical Sciences, University of Novi Sad, Novi Sad, Serbia;Faculty of Technical Sciences, University of Novi Sad, Novi Sad, Serbia;Faculty of Technical Sciences, University of Novi Sad, Novi Sad, Serbia
Venue:
Applied Intelligence
Year:
2012

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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.