Abstract
We describe an original implementation of k-Maximum Likelihood Estimator (k-MLE)[1], a fast algorithm for learning finite statistical mixtures of exponential families. Our version converges to a local maximum of the complete likelihood while guaranteeing not to have empty clusters. To initialize k-MLE, we propose a careful and greedy strategy inspired by k-means++ which selects automatically cluster centers and their number. The paper gives all details for using k-MLE with mixtures of Wishart (WMMs). Finally, we propose to use the Cauchy-Schwartz divergence as a comparison measure between two WMMs and give a general methodology for building a motion retrieval system.
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Saint-Jean, C., Nielsen, F. (2013). A New Implementation of k-MLE for Mixture Modeling of Wishart Distributions. In: Nielsen, F., Barbaresco, F. (eds) Geometric Science of Information. GSI 2013. Lecture Notes in Computer Science, vol 8085. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40020-9_26
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DOI: https://doi.org/10.1007/978-3-642-40020-9_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40019-3
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