Fuzzy and robust formulations of maximum-likelihood-based Gaussian mixture decomposition

Author

Choi, YoungSik ; Krishnapuram, Raghu

Author_Institution

Dept. of Comput. Eng. & Comput. Sci., Missouri Univ., Columbia, MO, USA

Volume

3

fYear

1996

fDate

8-11 Sep 1996

Firstpage

1899

Abstract

We show that maximum-likelihood-based Gaussian mixture decomposition (GMD) can be viewed as a probabilistic clustering algorithm. Furthermore, we formulate a fuzzy version of the GMD algorithm, and present the similarities and differences between the fuzzy C-means (FCM) algorithm and the fuzzy GMD method. In order to provide a good initial point, we propose a new initialization method for the fuzzy GMD algorithm. We also derive the objective function and update equations for a robust version of the FCM and the fuzzy GMD. The robust versions can be used when the data set is expected to be noisy

Keywords

maximum likelihood estimation; fuzzy C-means algorithm; initialization method; maximum-likelihood-based Gaussian mixture decomposition; objective function; probabilistic clustering algorithm; update equations; Clustering algorithms; Clustering methods; Equations; Least squares approximation; Maximum likelihood estimation; Neural networks; Parameter estimation; Partitioning algorithms; Robustness; Working environment noise;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems, 1996., Proceedings of the Fifth IEEE International Conference on

Conference_Location

New Orleans, LA

Print_ISBN

0-7803-3645-3

Type

conf

DOI

10.1109/FUZZY.1996.552688

Filename

552688