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Latent dirichlet allocation

by David M. Blei, Andrew Y. Ng, Michael I. Jordan, John Lafferty - Journal of Machine Learning Research , 2003
"... We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of discrete data such as text corpora. LDA is a three-level hierarchical Bayesian model, in which each item of a collection is modeled as a finite mixture over an underlying set of topics. Each topic is, ..."
Abstract - Cited by 4365 (92 self) - Add to MetaCart
We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of discrete data such as text corpora. LDA is a three-level hierarchical Bayesian model, in which each item of a collection is modeled as a finite mixture over an underlying set of topics. Each topic is

Spatial Latent Dirichlet Allocation

by Xiaogang Wang, Eric Grimson , 2007
"... In recent years, the language model Latent Dirichlet Allocation (LDA), which clusters co-occurring words into topics, has been widely applied in the computer vision field. However, many of these applications have difficulty with modeling the spatial and temporal structure among visual words, since L ..."
Abstract - Cited by 46 (3 self) - Add to MetaCart
In recent years, the language model Latent Dirichlet Allocation (LDA), which clusters co-occurring words into topics, has been widely applied in the computer vision field. However, many of these applications have difficulty with modeling the spatial and temporal structure among visual words, since

Latent Dirichlet Allocation in R

by Martin Ponweiser , 2012
"... ... werden Dokumentenkorpora in statistische Modelle überführt und sind dadurch besser durchsuch- und erforschbar. Das bekannteste Topic Modell ist Latent Dirichlet Al-location (LDA), das sich seit seiner Einführung im Jahre 2003 durch Blei et al. als nützliches Werkzeug in verschiedenen Diszipl ..."
Abstract - Cited by 3 (1 self) - Add to MetaCart
... werden Dokumentenkorpora in statistische Modelle überführt und sind dadurch besser durchsuch- und erforschbar. Das bekannteste Topic Modell ist Latent Dirichlet Al-location (LDA), das sich seit seiner Einführung im Jahre 2003 durch Blei et al. als nützliches Werkzeug in verschiedenen

Collective Latent Dirichlet Allocation

by Zhi-yong Shen, Yi-dong Shen
"... In this paper, we propose a new variant of Latent Dirichlet Allocation(LDA): Collective LDA (C-LDA), for multiple corpora modeling. C-LDA combines multiple corpora during learning such that it can transfer knowledge from one corpus to another; meanwhile it keeps a discriminative node which represent ..."
Abstract - Cited by 1 (0 self) - Add to MetaCart
In this paper, we propose a new variant of Latent Dirichlet Allocation(LDA): Collective LDA (C-LDA), for multiple corpora modeling. C-LDA combines multiple corpora during learning such that it can transfer knowledge from one corpus to another; meanwhile it keeps a discriminative node which

The Security of Latent Dirichlet Allocation

by Shike Mei, Xiaojin Zhu
"... Latent Dirichlet allocation (LDA) is an in-creasingly popular tool for data analysis in many domains. If LDA output affects de-cision making (especially when money is in-volved), there is an incentive for attackers to compromise it. We ask the question: how can an attacker minimally poison the corpu ..."
Abstract - Cited by 1 (1 self) - Add to MetaCart
Latent Dirichlet allocation (LDA) is an in-creasingly popular tool for data analysis in many domains. If LDA output affects de-cision making (especially when money is in-volved), there is an incentive for attackers to compromise it. We ask the question: how can an attacker minimally poison

Online Learning for Latent Dirichlet Allocation

by Matthew D. Hoffman, David M. Blei, Francis Bach
"... We develop an online variational Bayes (VB) algorithm for Latent Dirichlet Allocation (LDA). Online LDA is based on online stochastic optimization with a natural gradient step, which we show converges to a local optimum of the VB objective function. It can handily analyze massive document collection ..."
Abstract - Cited by 209 (21 self) - Add to MetaCart
We develop an online variational Bayes (VB) algorithm for Latent Dirichlet Allocation (LDA). Online LDA is based on online stochastic optimization with a natural gradient step, which we show converges to a local optimum of the VB objective function. It can handily analyze massive document

Latent Dirichlet Allocation

by Nguyễn Xuân Long, Gibbs Sampling, Variational Inference , 2012
"... Geometry of space of probability measures Posterior concentration theorems ..."
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Geometry of space of probability measures Posterior concentration theorems

Scalable Inference for Latent Dirichlet Allocation

by James Petterson, Tibério Caetano , 909
"... We investigate the problem of learning a topic model – the well-known Latent Dirichlet Allocation – in a distributed manner, using a cluster of C processors and dividing the corpus to be learned equally among them. We propose a simple approximated method that can be tuned, trading speed for accuracy ..."
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We investigate the problem of learning a topic model – the well-known Latent Dirichlet Allocation – in a distributed manner, using a cluster of C processors and dividing the corpus to be learned equally among them. We propose a simple approximated method that can be tuned, trading speed

Word Features for Latent Dirichlet Allocation

by James Petterson, Alex Smola, Tiberio Caetano, Wray Buntine, Shravan Narayanamurthy
"... 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 dictiona ..."
Abstract - Cited by 19 (3 self) - Add to MetaCart
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

Authorship Attribution with Latent Dirichlet Allocation

by Yanir Seroussi, Ingrid Zukerman, Fabian Bohnert
"... The problem of authorship attribution – attributing texts to their original authors – has been an active research area since the end of the 19th century, attracting increased interest in the last decade. Most of the work on authorship attribution focuses on scenarios with only a few candidate author ..."
Abstract - Cited by 5 (0 self) - Add to MetaCart
authors, but recently considered cases with tens to thousands of candidate authors were found to be much more challenging. In this paper, we propose ways of employing Latent Dirichlet Allocation in authorship attribution. We show that our approach yields state-of-the-art performance for both a few
Results 1 - 10 of 1,184