A Multilinear Singular Value Decomposition
SIAM Journal on Matrix Analysis and Applications
Modern Information Retrieval
Term Weighting Approaches in Automatic Text Retrieval
Term Weighting Approaches in Automatic Text Retrieval
Language Modeling for Information Retrieval
Language Modeling for Information Retrieval
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MM '08 Proceedings of the 16th ACM international conference on Multimedia
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IEEE Transactions on Multimedia
Tensor Framework and Combined Symmetry for Hypertext Mining
Fundamenta Informaticae
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IDEAL'10 Proceedings of the 11th international conference on Intelligent data engineering and automated learning
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FDIA'09 Proceedings of the Third BCS-IRSG conference on Future Directions in Information Access
A New Truncation Strategy for the Higher-Order Singular Value Decomposition
SIAM Journal on Scientific Computing
Tensor Framework and Combined Symmetry for Hypertext Mining
Fundamenta Informaticae
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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.