2 research outputs found
Adaptive Seeding for Gaussian Mixture Models
We present new initialization methods for the expectation-maximization
algorithm for multivariate Gaussian mixture models. Our methods are adaptions
of the well-known -means++ initialization and the Gonzalez algorithm.
Thereby we aim to close the gap between simple random, e.g. uniform, and
complex methods, that crucially depend on the right choice of hyperparameters.
Our extensive experiments indicate the usefulness of our methods compared to
common techniques and methods, which e.g. apply the original -means++ and
Gonzalez directly, with respect to artificial as well as real-world data sets.Comment: This is a preprint of a paper that has been accepted for publication
in the Proceedings of the 20th Pacific Asia Conference on Knowledge Discovery
and Data Mining (PAKDD) 2016. The final publication is available at
link.springer.com (http://link.springer.com/chapter/10.1007/978-3-319-31750-2
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