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Abstract:
We describe a new iterative method for parameter estimation
of Gaussian mixtures. The new method is based on a framework
developed by Kivinen and Warmuth for supervised on-line learning.
In contrast to gradient descent and EM, which estimate the
mixture's covariance matrices, the proposed method estimates the
inverses of the covariance matrices. Furthermore, the new parameter
estimation procedure can be applied in both on-line and batch
settings. We show experimentally that it is typically faster than
EM, and usually requires about half as many iterations as EM. We
also describe experiments with digit recognition that demonstrate
the merits of the on-line version.
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