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CIKM
2010
Springer
13 years 8 months ago
Decomposing background topics from keywords by principal component pursuit
Low-dimensional topic models have been proven very useful for modeling a large corpus of documents that share a relatively small number of topics. Dimensionality reduction tools s...
Kerui Min, Zhengdong Zhang, John Wright, Yi Ma
ACCV
2010
Springer
14 years 6 days ago
Identifying Surprising Events in Videos Using Bayesian Topic Models
Automatic processing of video data is essential in order to allow efficient access to large amounts of video content, a crucial point in such applications as video mining and surve...
Avishai Hendel, Daphna Weinshall, Shmuel Peleg
CVPR
2007
IEEE
14 years 12 months ago
Unsupervised Activity Perception by Hierarchical Bayesian Models
We propose a novel unsupervised learning framework for activity perception. To understand activities in complicated scenes from visual data, we propose a hierarchical Bayesian mod...
Xiaogang Wang, Xiaoxu Ma, Eric Grimson
PAMI
2010
113views more  PAMI 2010»
13 years 8 months ago
Hierarchical Bayesian Modeling of Topics in Time-Stamped Documents
—We consider the problem of inferring and modeling topics in a sequence of documents with known publication dates. The documents at a given time are each characterized by a topic...
Iulian Pruteanu-Malinici, Lu Ren, John William Pai...
JMLR
2006
138views more  JMLR 2006»
13 years 9 months ago
Noisy-OR Component Analysis and its Application to Link Analysis
We develop a new component analysis framework, the Noisy-Or Component Analyzer (NOCA), that targets high-dimensional binary data. NOCA is a probabilistic latent variable model tha...
Tomás Singliar, Milos Hauskrecht