Results 1 - 10 of 259

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

The Author-Topic Model for Authors and Documents

by Michal Rosen-Zvi, Thomas Griffiths, Mark Steyvers, Padhraic Smyth
"... We introduce the author-topic model, a generative model for documents that extends Latent Dirichlet Allocation (LDA; Blei, Ng, & Jordan, 2003) to include authorship information. Each author is associated with a multinomial distribution over topics and each topic is associated with a multinomial ..."
Abstract - Cited by 366 (18 self) - Add to MetaCart
We introduce the author-topic model, a generative model for documents that extends Latent Dirichlet Allocation (LDA; Blei, Ng, & Jordan, 2003) to include authorship information. Each author is associated with a multinomial distribution over topics and each topic is associated with a multinomial

DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification

by Simon Lacoste-julien, Fei Sha, Michael I. Jordan
"... Probabilistic topic models have become popular as methods for dimensionality reduction in collections of text documents or images. These models are usually treated as generative models and trained using maximum likelihood or Bayesian methods. In this paper, we discuss an alternative: a discriminativ ..."
Abstract - Cited by 115 (0 self) - Add to MetaCart
that uncovers the latent structure in a document collection while preserving predictive power for the task of classification. We compare the predictive power of the latent structure of DiscLDA with unsupervised LDA on the 20 Newsgroups document classification task and show how our model can identify shared

Discovering object categories in image collections

by Josef Sivic, Bryan C. Russell, Alexei A. Efros, Andrew Zisserman, William T. Freeman , 2004
"... Given a set of images containing multiple object categories, we seek to discover those categories and their image locations without supervision. We achieve this using generative models from the statistical text literature: probabilistic Latent Semantic Analysis (pLSA), and Latent Dirichlet Allocatio ..."
Abstract - Cited by 197 (12 self) - Add to MetaCart
Allocation (LDA). In text analysis these are used to discover topics in a corpus using the bag-of-words document representation. Here we discover topics as object categories, so that an image containing instances of several categories is modelled as a mixture of topics. The models are applied to images

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
collections, including those arriving in a stream. We study the performance of online LDA in several ways, including by fitting a 100-topic topic model to 3.3M articles from Wikipedia in a single pass. We demonstrate that online LDA finds topic models as good or better than those found with batch VB, and in a

LDA+ TCP-Friendly Adaptation: A Measurement and Comparison Study

by Dorgham Sisalem, Adam Wolisz - in the 10th International Workshop on Network and Operating Systems Support for Digital Audio and Video (NOSSDAV'2000 , 2000
"... Abstract — In this paper, we present an end-to-end adaptation scheme, called the enhanced loss-delay based adaptation algorithm (LDA+) for regulating the transmission behavior of multimedia senders in accordance with the network congestion state. LDA+ uses the real-time transport protocol (RTP) for ..."
Abstract - Cited by 61 (0 self) - Add to MetaCart
) for collecting loss and delay statistics which are then used for adjusting the transmission behavior of the senders in a manner similar to TCP connections suffering from equal losses and delays. The performance of LDA+ is then investigated by running several simulations as well as measurements over the Internet

Topic Models (pLSA LDA);

by Felix X. Yu, Rongrong Ji, Ming-hen Tsai, Guangnan Ye, Shih-fu Chang
"... • MARR relies only on the pre-labeled attributes to design the dependency model. • User labeling is a burdensome process. The small amount of attributes are far from sufficient in forming an expressive feature space. 2. Our Solution • Weak Attributes are a collection of mid-level representations, wh ..."
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• MARR relies only on the pre-labeled attributes to design the dependency model. • User labeling is a burdensome process. The small amount of attributes are far from sufficient in forming an expressive feature space. 2. Our Solution • Weak Attributes are a collection of mid-level representations

Topic-Link LDA: Joint Models of Topic and Author Community

by Yan Liu, Alexandru Niculescu-mizil, Wojciech Gryc , 2009
"... Given a large-scale linked document collection, such as a collection of blog posts or a research literature archive, there are two fundamental problems that have generated a lot of interest in the research community. One is to identify a set of high-level topics covered by the documents in the colle ..."
Abstract - Cited by 54 (1 self) - Add to MetaCart
Given a large-scale linked document collection, such as a collection of blog posts or a research literature archive, there are two fundamental problems that have generated a lot of interest in the research community. One is to identify a set of high-level topics covered by the documents

Topic significance ranking of LDA generative models

by Loulwah Alsumait, Daniel Barbará, James Gentle, Carlotta Domeniconi - In Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD , 2009
"... Abstract. Topic models, like Latent Dirichlet Allocation (LDA), have been recently used to automatically generate text corpora topics, and to subdivide the corpus words among those topics. However, not all the estimated topics are of equal importance or correspond to genuine themes of the domain. So ..."
Abstract - Cited by 25 (1 self) - Add to MetaCart
. Some of the topics can be a collection of irrelevant or background words, or represent insignificant themes. Current approaches to topic modeling perform manual examination of their output to find meaningful and important topics. This paper presents the first automated unsupervised analysis of LDA

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
Results 1 - 10 of 259