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Authors: Amr Ahmed Sujith Ravi Shravan M. Narayanamurthy Alexander J. Smola
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Abstract
Clustering is a key component in any data analysis toolbox. Despite its importance, scalable algorithms often eschew rich statistical models in favor of simpler descriptions such as k -means clustering. In this paper we present a sampler, capable of estimating mixtures of exponential families. At its heart lies a novel proposal distribution using random projections to achieve high throughput in generating proposals, which is crucial for clustering models with large numbers of clusters.
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Update: last updated 12/09/2012, 09:37 PM


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