Latent semantic indexing (LSI) is an information retrieval technique based on the spectral analysis of the term-document matrix, whose empirical success had heretofore been without rigorous prediction and explanation. We prove that, under certain conditions, LSI does succeed in capturing the underly
โฆ LIBER โฆ
Symmetrization and overfitting in probabilistic latent semantic analysis
โ Scribed by V. A. Leksin
- Book ID
- 110209641
- Publisher
- SP MAIK Nauka/Interperiodica
- Year
- 2009
- Tongue
- English
- Weight
- 246 KB
- Volume
- 19
- Category
- Article
- ISSN
- 1054-6618
No coin nor oath required. For personal study only.
๐ SIMILAR VOLUMES
Latent Semantic Indexing: A Probabilisti
โ
Christos H. Papadimitriou; Prabhakar Raghavan; Hisao Tamaki; Santosh Vempala
๐
Article
๐
2000
๐
Elsevier Science
๐
English
โ 182 KB
Unsupervised Learning by Probabilistic L
โ
Thomas Hofmann
๐
Article
๐
2001
๐
Springer
๐
English
โ 203 KB
Multi-view learning via probabilistic la
โ
Fuzhen Zhuang; George Karypis; Xia Ning; Qing He; Zhongzhi Shi
๐
Article
๐
2012
๐
Elsevier Science
๐
English
โ 315 KB
Action categorization by structural prob
โ
Jianguo Zhang; Shaogang Gong
๐
Article
๐
2010
๐
Elsevier Science
๐
English
โ 445 KB
A probabilistic model for Latent Semanti
โ
Chris H.Q. Ding
๐
Article
๐
2005
๐
John Wiley and Sons
๐
English
โ 183 KB
๐ 1 views
## Abstract Latent Semantic Indexing (LSI), when applied to semantic space built on text collections, improves information retrieval, information filtering, and word sense disambiguation. A new dual probability model based on the similarity concepts is introduced to provide deeper understanding of
Latent semantic analysis and Fiedler ret
โ
Bruce Hendrickson
๐
Article
๐
2007
๐
Elsevier Science
๐
English
โ 359 KB