FEMA: A Fast Expectation Maximization Algorithm based on Grid and PCA

Author

Yu, Zhiwen ; Wong, Hau-San

Author_Institution

Dept. of Comput. Sci., City Univ. of Hong Kong

fYear

2006

fDate

9-12 July 2006

Firstpage

1913

Lastpage

1916

Abstract

EM algorithm is an important unsupervised clustering algorithm, but the algorithm has several limitations. In this paper, we propose a fast EM algorithm (FEMA) to address the limitations of EM and enhance its efficiency. FEMA achieves low running time by combining principal component analysis (PCA), a grid cell expansion algorithm (GCEA) and a hierarchical cluster tree. PCA and multi-dimensional grid are applied to find a set of "good" initial parameters for the EM algorithm, while the hierarchical cluster tree deals with the case where the cluster is concave by making use of a merging algorithm. The experiments indicate that FEMA outperforms EM by reducing 45% of the CPU time

Keywords

expectation-maximisation algorithm; image segmentation; pattern clustering; principal component analysis; tree data structures; unsupervised learning; FEMA; GCEA; PCA; fast expectation maximization algorithm; grid cell expansion algorithm; hierarchical cluster tree; merging algorithm; principal component analysis; unsupervised clustering algorithm; Algorithm design and analysis; Clustering algorithms; Computer science; Covariance matrix; Eigenvalues and eigenfunctions; Machine learning algorithms; Merging; Multimedia databases; Partitioning algorithms; Principal component analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Multimedia and Expo, 2006 IEEE International Conference on

Conference_Location

Toronto, Ont.

Print_ISBN

1-4244-0366-7

Electronic_ISBN

1-4244-0367-7

Type

conf

DOI

10.1109/ICME.2006.262930

Filename

4036999