Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/39822
Title: Fast dimension reduction for document classification based on imprecise spectrum analysis
Authors: Guan, HU
Xiao, B 
Zhou, J
Guo, M
Yang, T
Keywords: Dimension reduction
Feature selection
LSI
SVD
Issue Date: 2010
Source: CIKM '10 Proceedings of the 19th ACM International Conference on Information and Knowledge Management, Toronto Canada, Oct. , 2010, p. 1753-1756 How to cite?
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.
URI: http://hdl.handle.net/10397/39822
ISBN: 978-1-4503-0099-5
DOI: 10.1145/1871437.1871721
Appears in Collections:Conference Paper

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

2
Last Week
0
Last month
Citations as of Jan 14, 2018

Page view(s)

39
Last Week
1
Last month
Citations as of Jan 22, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.