Pólya Urn Latent Dirichlet Allocation: A Doubly Sparse Massively Parallel Sampler

Latent Dirichlet Allocation (LDA) is a topic model widely used in natural language processing and machine learning. Most approaches to training the model rely on iterative algorithms, which makes it difficult to run LDA on big corpora that are best analyzed in parallel and distributed computational...

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Veröffentlicht in:	IEEE transactions on pattern analysis and machine intelligence 2019-07, Vol.41 (7), p.1709-1719
Hauptverfasser:	Terenin, Alexander, Magnusson, Mans, Jonsson, Leif, Draper, David
Format:	Artikel
Sprache:	eng
Schlagworte:	Approximation Bayesian inference big data Color Complexity theory computational complexity Computational modeling Convergence Current distribution Dirichlet problem gibbs sampling Iterative algorithms Iterative methods latent dirichlet allocation Machine learning markov chain monte carlo Mathematical analysis Natural language processing parallel and distributed systems Parallel processing Probability Resource management Samplers topic models
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