2006 IEEE International Conference on Multimedia and Expo FEMA: A Fast Expectation Maximization Algorithm based on Grid and PCA Toronto, ON, Canada July 09-July 12 ISBN: 1-4244-0366-7
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.
Citation:
Zhiwen Yu, Hau-san Wong, "FEMA: A Fast Expectation Maximization Algorithm based on Grid and PCA," icme, pp.1913-1916, 2006 IEEE International Conference on Multimedia and Expo, 2006 Usage of this product signifies your acceptance of the Terms of Use. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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