FastEx: Hash Clustering with Exponential Families

Part of Advances in Neural Information Processing Systems 25 (NIPS 2012)

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Amr Ahmed, Sujith Ravi, Alex Smola, Shravan Narayanamurthy


Clustering is a key component in 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.