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Contents

SVD-LDA : a combined model for text classification / Nguyen Cao Truong Hai ; Kyung-Im Kim ; Hyuk-Ro Park 1

Abstract 1

1. Introduction 1

2. Related Work and Motivation 1

2.1. Latent Semantic Analysis by Singular Value Decomposition 1

2.2. Probabilistic Latent Semantic Analysis 2

2.3. Latent Dirichlet Allocation 2

2.4. Motivation 3

3. The Combined Model 3

4. Experiments 4

5. Conclusions 5

References 5

[저자소개] 5

초록보기

Text data has always accounted for a major portion of the world's information. As the volume of information increases exponentially, the portion of text data also increases significantly. Text classification is therefore still an important area of research. LDA is an updated, probabilistic model which has been used in many applications in many other fields. As regards text data, LDA also has many applications, which has been applied various enhancements. However, it seems that no applications take care of the input for LDA. In this paper, we suggest a way to map the input space to a reduced space, which may avoid the unreliability, ambiguity and redundancy of individual terms as descriptors. The purpose of this paper is to show that LDA can be perfectly performed in a "clean and clear" space. Experiments are conducted on 20 News Groups data sets. The results show that the proposed method can boost the classification results when the appropriate choice of rank of the reduced space is determined.

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기사명 저자명 페이지 원문 목차
A new variational level set evolving algorithm for image segmentation Yang Fei ;Jong Won Park pp.1-4

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SVD-LDA: a combined model for text classification Nguyen Cao Truong Hai ;Kyung-Im Kim ;Hyuk-Ro Park pp.5-10

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An efficient web ontology storage considering hierarchical knowledge for Jena-based applications Dongwon Jeong ;Heeyoung Shin ;Doo-Kwon Baik ;Young-Sik Jeong pp.11-18

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Privacy-aware adaptable web services using petri nets You-Jin Song ;Jae-Geol Yim pp.19-24

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Implementation of advanced IP network technology for IPTV service Young-Do Joo pp.25-32

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Dynamic control of random constant spreading worm using depth distribution characteristics Byung-Gyu No ;Doo-Soon Park ;Min Hong ;HwaMin Lee ;Yoon Sok Park pp.33-40

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참고문헌 (18건) : 자료제공( 네이버학술정보 )

참고문헌 목록에 대한 테이블로 번호, 참고문헌, 국회도서관 소장유무로 구성되어 있습니다.
번호 참고문헌 국회도서관 소장유무
1 Zhiwei Zhang, Xuan-Hieu Phan, Susumu Horiguchi, “An Efficient Feature Selection using Hidden Topics in Text Categorization,” 22nd International Conference on Advanced Information Networking and Application, 2008. 미소장
2 A maximum entropy approach to natural language processing 네이버 미소장
3 S. Deerwester, G. W. Furnas, and T. K. Landauer, “Indexing by latent semantic analysis,” Journal of the American Society for Info, Science, Vol.41, No.6, 1990, pp.391-407. 미소장
4 D. M. Blei, A. Ng, and M. I. Jordan, “Latent Dirichlet Allocation,” JMLR, Vol.3, 2003, pp.993-1022. 미소장
5 Ramesh Nallapati and William Cohen, “Link-PLSALDA: A new unsupervised model for topics and the influence of blogs,” AAAI, 2008. 미소장
6 G. Heinrich, “Parameter estimation for text analysis,” Technical report - University of Leipzig, Germany, 2005. 미소장
7 T. Hofmann, “Probabilistic latent semantic indexing,” Proceedings of SIGIR'99, 1999. 미소장
8 Applying Latent Dirichlet Allocation to Automatic Essay Grading 네이버 미소장
9 Machine learning in automated text categorization 네이버 미소장
10 Y. Yang and J. O. Pedersen, “A Comparative Study on Feature Selection in Text Categorization,” Proceedings of the Fourteenth International Conference on Machine Learning, 1997, pp.412-420. 미소장
11 An Introduction to MCMC for Machine Learning 네이버 미소장
12 T. Hofmann, J. Puzicha, and M. I. Jordan, “Unsupervised learning from dyadic data,” Advances in Neural Information Processing Systems, Volume 11. MIT Press, 1999. 미소장
13 B.C. Russell, A.A. Efros, J. Sivic, W.T. Freeman, and A. Zisserman, "Using Multiple Segmentations to Discover Objects and their Extent in Image Collections,” Proceedings of CVPR, June, 2006. 미소장
14 Latent semantic models for collaborative filtering 네이버 미소장
15 T. Minka and J. Lafferty, “Expectation-propagation for the generative aspect model,” Proc. UAI, 2002. 미소장
16 Machine learning in automated text categorization 네이버 미소장
17 http://www.puffinwarellc.com/p3b.htm. 미소장
18 http://en.wikipedia.org/wiki/Information_retrieval. 미소장

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