Fifth IEEE International Conference on Data Mining (ICDM'05) (2005)
Nov. 27, 2005 to Nov. 30, 2005
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2005.144
Ning Liu , Tsinghua University
Benyu Zhang , Microsoft Research Asia
Jun Yan , Peking University
Zheng Chen , Microsoft Research Asia
Wenyin Liu , City University of Hong Kong
Fengshan Bai , Tsinghua University
Leefeng Chien , Academia Sinica
In this paper, we propose a text representation model, Tensor Space Model (TSM), which models the text by multilinear algebraic high-order tensor instead of the traditional vector. Supported by techniques of multilinear algebra, TSM offers a potent mathematical framework for analyzing the multifactor structures. TSM is further supported by certain introduced particular operations and presented tools, such as the High-Order Singular Value Decomposition (HOSVD) for dimension reduction and other applications. Experimental results on the 20 Newsgroups dataset show that TSM is constantly better than VSM for text classification.
W. Liu et al., "Text Representation: From Vector to Tensor," Fifth IEEE International Conference on Data Mining (ICDM'05)(ICDM), Houston, Texas, 2005, pp. 725-728.