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» Statistical Debugging Using Latent Topic Models
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KDD
2010
ACM
435views Data Mining» more  KDD 2010»
13 years 8 months ago
Topic models with power-law using Pitman-Yor process
One of the important approaches for Knowledge discovery and Data mining is to estimate unobserved variables because latent variables can indicate hidden and specific properties o...
Issei Sato, Hiroshi Nakagawa
ICML
2006
IEEE
14 years 5 months ago
Topic modeling: beyond bag-of-words
Some models of textual corpora employ text generation methods involving n-gram statistics, while others use latent topic variables inferred using the "bag-of-words" assu...
Hanna M. Wallach
BIBM
2010
IEEE
151views Bioinformatics» more  BIBM 2010»
13 years 2 months ago
Probabilistic topic modeling for genomic data interpretation
Recently, the concept of a species containing both core and distributed genes, known as the supra- or pangenome theory, has been introduced. In this paper, we aim to develop a new ...
Xin Chen, Xiaohua Hu, Xiajiong Shen, Gail Rosen
CIKM
2008
Springer
13 years 6 months ago
Modeling hidden topics on document manifold
Topic modeling has been a key problem for document analysis. One of the canonical approaches for topic modeling is Probabilistic Latent Semantic Indexing, which maximizes the join...
Deng Cai, Qiaozhu Mei, Jiawei Han, Chengxiang Zhai
NIPS
2007
13 years 6 months ago
Supervised Topic Models
We introduce supervised latent Dirichlet allocation (sLDA), a statistical model of labelled documents. The model accommodates a variety of response types. We derive a maximum-like...
David M. Blei, Jon D. McAuliffe