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Fifth IEEE International Conference on Data Mining (ICDM'05)
Text Representation: From Vector to Tensor
Houston, Texas
November 27-November 30
ISBN: 0-7695-2278-5
ASCII Text
x 
 
Ning Liu, Benyu Zhang, Jun Yan, Zheng Chen, Wenyin Liu, Fengshan Bai, Leefeng Chien, "Text Representation: From Vector to Tensor," Data Mining, IEEE International Conference on, pp. 725-728, Fifth IEEE International Conference on Data Mining (ICDM'05), 2005.
 
 
BibTex
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@article{ 10.1109/ICDM.2005.144,
author = {Ning Liu and Benyu Zhang and Jun Yan and Zheng Chen and Wenyin Liu and Fengshan Bai and Leefeng Chien},
title = {Text Representation: From Vector to Tensor},
journal ={Data Mining, IEEE International Conference on},
volume = {0},
year = {2005},
issn = {1550-4786},
pages = {725-728},
doi = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2005.144},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Data Mining, IEEE International Conference on
TI - Text Representation: From Vector to Tensor
SN - 1550-4786
SP725
EP728
A1 - Ning Liu,
A1 - Benyu Zhang,
A1 - Jun Yan,
A1 - Zheng Chen,
A1 - Wenyin Liu,
A1 - Fengshan Bai,
A1 - Leefeng Chien,
PY - 2005
KW - null
VL - 0
JA - Data Mining, IEEE International Conference on
ER -
 
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.
Citation:
Ning Liu, Benyu Zhang, Jun Yan, Zheng Chen, Wenyin Liu, Fengshan Bai, Leefeng Chien, "Text Representation: From Vector to Tensor," icdm, pp.725-728, Fifth IEEE International Conference on Data Mining (ICDM'05), 2005
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