algorithm employs a cluster model based on kernel density estimation. A cluster is defined by a local maximum of the estimated density function. Data points are assigned to clusters by hill climbing, i.e. points going to the same local maximum are put into the same cluster. A disadvantage of
1.0 is, that the used hill climbing may make unnecessary small steps in the beginning and never converges exactly to the maximum, it just comes close.
We introduce a new hill climbing procedure for Gaussian kernels, which adjusts the step size automatically at no extra costs. We prove that the procedure converges exactly towards a local maximum by reducing it to a special case of the expectation maximization algorithm. We show experimentally that the new procedure needs much less iterations and can be accelerated by sampling based methods with sacrificing only a small amount of accuracy.
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