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Shravan Narayanamurthy

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2 papers

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NeurIPS Conference 2012 Conference Paper

FastEx: Hash Clustering with Exponential Families

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.

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NeurIPS Conference 2010 Conference Paper

Word Features for Latent Dirichlet Allocation

James Petterson
Wray Buntine
Shravan Narayanamurthy
Tibério Caetano
Alex Smola

We extend Latent Dirichlet Allocation (LDA) by explicitly allowing for the encoding of side information in the distribution over words. This results in a variety of new capabilities, such as improved estimates for infrequently occurring words, as well as the ability to leverage thesauri and dictionaries in order to boost topic cohesion within and across languages. We present experiments on multi-language topic synchronisation where dictionary information is used to bias corresponding words towards similar topics. Results indicate that our model substantially improves topic cohesion when compared to the standard LDA model.

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