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| Content Provider | IEEE Xplore Digital Library | 
|---|---|
| Author | Yan, J. Ning Liu Qiang Yang Weiguo Fan Zheng Chen | 
| Copyright Year | 2008 | 
| Description | Author affiliation: Microsoft Res. Asia, Sigma Center, Beijing (Yan, J.; Ning Liu; Zheng Chen) || Dept. of Comput. Sci., Hong Kong Univ. of Sci. & Technol., Kowloon (Qiang Yang) || Dept. of Comput. Sci., Virginia Polytech. Inst. & State Univ., Blacksburg, VA (Weiguo Fan) | 
| Abstract | Dimension reduction for large-scale text data is attracting much attention lately due to the rapid growth of World Wide Web. We can consider dimension reduction algorithms in two categories: feature extraction and feature selection. An important problem remains: it has been difficult to integrate these two algorithm categories into a single framework, making it difficult to reap the benefit of both. In this paper, we formulate the two algorithm categories through a unified optimization framework. Under this framework, we develop a novel feature selection algorithm called Trace Oriented Feature Analysis (TOFA). The novel objective function of TOFA is a unified framework that integrates many prominent feature extraction algorithms such as unsupervised Principal Component Analysis and supervised Maximum Margin Criterion are special cases of it. Thus TOFA can process not only supervised problem but also unsupervised and semi-supervised problems. Experimental results on real text datasets demonstrate the effectiveness and efficiency of TOFA. | 
| Starting Page | 668 | 
| Ending Page | 677 | 
| File Size | 277448 | 
| Page Count | 10 | 
| File Format | |
| ISBN | 9780769535029 | 
| ISSN | 15504786 | 
| DOI | 10.1109/ICDM.2008.67 | 
| Language | English | 
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) | 
| Publisher Date | 2008-12-15 | 
| Publisher Place | Italy | 
| Access Restriction | Subscribed | 
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) | 
| Subject Keyword | Text categorization Feature extraction Large-scale systems Principal component analysis Computer science Algorithm design and analysis Text processing Data mining Asia USA Councils Text Categorization Feature Analysis | 
| Content Type | Text | 
| Resource Type | Article | 
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