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Content Provider | ACM Digital Library |
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Author | Xiao, Bin Guo, Minyi Guan, Hu Yang, Tao Zhou, Jingyu |
Abstract | This paper proposes an algorithm called Imprecise Spectrum Analysis (ISA) to carry out fast dimension reduction for document classification. ISA is designed based on the one-sided Jacobi method for Singular Value Decomposition (SVD). To speedup dimension reduction, it simplifies the orthogonalization process of Jacobi computation and introduces a new mapping formula for transforming original document-term vectors. To improve classification accuracy using ISA, a feature selection method is further developed to make inter-class feature vectors more orthogonal in building the initial weighted term-document matrix. Our experimental results show that ISA is extremely fast in handling large term-document matrices and delivers better or competitive classification accuracy compared to SVD-based LSI. |
Starting Page | 1753 |
Ending Page | 1756 |
Page Count | 4 |
File Format | |
ISBN | 9781450300995 |
DOI | 10.1145/1871437.1871721 |
Language | English |
Publisher | Association for Computing Machinery (ACM) |
Publisher Date | 2010-10-26 |
Publisher Place | New York |
Access Restriction | Subscribed |
Subject Keyword | Svd Dimension reduction Feature selection Lsi |
Content Type | Text |
Resource Type | Article |
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