Tensor Space Model for document analysis

Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval

Volumn 2006, Issue , 2006, Pages 625-626

  Cai, Deng ^a   He, Xiaofei ^b   Han, Jiawei ^a  

^a UIUC   (United States)

^b YAHOO RESEARCH   (United States)

Author keywords

Latent semantic indexing; Tensor latent semantic indexing; Tensor space model; Vector space model

Indexed keywords

ALGORITHMS; COMPUTATIONAL COMPLEXITY; MATHEMATICAL MODELS; SEMANTICS; TENSORS; SUPPORT VECTOR MACHINES; VECTORS;

LATENT SEMANTIC INDEXING; TENSOR LATENT SEMANTIC INDEXING; TENSOR SPACE MODEL; VECTOR SPACE MODEL;

INFORMATION RETRIEVAL; INFORMATION RETRIEVAL SYSTEMS;

EID: 33750374181     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1148170.1148287     Document Type: Conference Paper

References (4)

1. D. Cai, X. He, and J. Han. Tensor space model for document analysis. Technical report, Computer Science Department, UIUC. UIUCDCS-R-2006-2715, April 2006.
2. S. C. Deerwester, S. T. Dumais, T. K. Landauer, G. W. Furnas, and R. A. harshman. Indexing by latent semantic analysis. Journal of the American Society of Information Science, 41(6):391-407, 1990.
3. G. Salton, A. Wong, and C. S. Yang. A vector space model for information retrieval. Communications of the ACM, 18(11):613-620, 1975.
4. J. Ye. Generalized low rank approximations of matrices. Machine Learning, 61(1-3):167-191. Nov. 2005.