Appropriate initial component densities of mixture modeling for pattern recognition

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Some initial component densities are compared in a mixture model for pattern recognition. The EM algorithm is widely adopted in construction of a mixture density for approximating a class-conditional density. However, the algorithm is very sensitive to the number of component densities and the initial component densities themselves. The initial component densities are obtained by a clustering method. We report the results of comparison between clustering methods yielding non-overlapping clusters and methods yielding overlapping clusters.

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