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Latent topic model for image annotation by modeling topic correlation
2013 IEEE International Conference on Multimedia and Expo (ICME), 2013For the task of image annotation, traditional probabilistic topic models based on Latent Dirichlet Allocation (LDA) [1], assume that an image is a mixture of latent topics. An inevitable limitation of LDA is the inability to model topic correlation since topic proportions of an image are generated independently. Motivated by Correlated Topic Model (CTM)
Xing Xu +2 more
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Communications of the ACM, 2011
Surveying a suite of algorithms that offer a solution to managing large document archives.
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Surveying a suite of algorithms that offer a solution to managing large document archives.
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2009 Ninth IEEE International Conference on Data Mining, 2009
In this paper we propose the multirelational topic model (MRTM) for multiple types of link modeling such as citation and coauthor links in document networks. In the citation network, the MRTM models the citation link between each pair of documents as a binary variable conditioned on their topic distributions.
Chun-hung Li +3 more
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In this paper we propose the multirelational topic model (MRTM) for multiple types of link modeling such as citation and coauthor links in document networks. In the citation network, the MRTM models the citation link between each pair of documents as a binary variable conditioned on their topic distributions.
Chun-hung Li +3 more
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2015 International Conference on Information Technology (ICIT), 2015
Topic Modeling has been a useful tool for finding abstract topics (which are collections of words) governing a collection of documents. Each document is then expressed as a collection of generated topics. The most basic topic model is Latent Dirichlet Allocation (LDA).
Vineet Padmanabhan +3 more
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Topic Modeling has been a useful tool for finding abstract topics (which are collections of words) governing a collection of documents. Each document is then expressed as a collection of generated topics. The most basic topic model is Latent Dirichlet Allocation (LDA).
Vineet Padmanabhan +3 more
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Applying a Burst Model to Detect Bursty Topics in a Topic Model
2012This paper focuses on two types of modeling of information flow in news stream, namely, burst analysis and topic modeling. First, when one wants to detect a kind of topics that are paid much more attention than usual, it is usually necessary for him/her to carefully watch every article in news stream at every moment.
Tomohiro Fukuhara +6 more
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2015
Many chapters in this book illustrate that applying a statistical method such as latent semantic analysis (LSA; Landauer & Dumais, 1997; Landauer, Foltz, & Laham, 1998) to large databases can yield insight into human cognition. The LSA approach makes three claims: that semantic information can be derived from a word-document co-occurrence matrix; that ...
Mark Steyvers, Thomas L. Griffiths
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Many chapters in this book illustrate that applying a statistical method such as latent semantic analysis (LSA; Landauer & Dumais, 1997; Landauer, Foltz, & Laham, 1998) to large databases can yield insight into human cognition. The LSA approach makes three claims: that semantic information can be derived from a word-document co-occurrence matrix; that ...
Mark Steyvers, Thomas L. Griffiths
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