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2006
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Tensor space model for document analysis

10 years 4 months ago
Tensor space model for document analysis
Vector Space Model (VSM) has been at the core of information retrieval for the past decades. VSM considers the documents as vectors in high dimensional space. In such a vector space, techniques like Latent Semantic Indexing (LSI), Support Vector Machines (SVM), Naive Bayes, etc., can be then applied for indexing and classification. However, in some cases, the dimensionality of the document space might be extremely large, which makes these techniques infeasible due to the curse of dimensionality. In this paper, we propose a novel Tensor Space Model for document analysis. We represent documents as the second order tensors, or matrices. Correspondingly, a novel indexing algorithm called Tensor Latent Semantic Indexing (TensorLSI) is developed in the tensor space. Our theoretical analysis shows that TensorLSI is much more computationally efficient than the conventional Latent Semantic Indexing, which makes it applicable for extremely large scale data set. Several experimental results on ...
Deng Cai, Xiaofei He, Jiawei Han
Added 14 Jun 2010
Updated 14 Jun 2010
Type Conference
Year 2006
Where SIGIR
Authors Deng Cai, Xiaofei He, Jiawei Han
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