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http://dx.doi.org/10.3745/KIPSTD.2012.19D.2.147

A Semi-supervised Dimension Reduction Method Using Ensemble Approach  

Park, Cheong-Hee (충남대학교 컴퓨터공학과)
Publication Information
The KIPS Transactions:PartD / v.19D, no.2, 2012 , pp. 147-150 More about this Journal
Abstract
While LDA is a supervised dimension reduction method which finds projective directions to maximize separability between classes, the performance of LDA is severely degraded when the number of labeled data is small. Recently semi-supervised dimension reduction methods have been proposed which utilize abundant unlabeled data and overcome the shortage of labeled data. However, matrix computation usually used in statistical dimension reduction methods becomes hindrance to make the utilization of a large number of unlabeled data difficult, and moreover too much information from unlabeled data may not so helpful compared to the increase of its processing time. In order to solve these problems, we propose an ensemble approach for semi-supervised dimension reduction. Extensive experimental results in text classification demonstrates the effectiveness of the proposed method.
Keywords
Linear Discriminant Analysis; Semi-Supervised Dimension Reduction; Text Classification; Ensemble Method;
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  • Reference
1 Y. Song, F. Nie, C. Zhang, and S. Xiang, "A unified framework for semi-supervised dimensionality reduction," Pattern recognition, Vol.41, pp.2789-2799, 2008.   DOI   ScienceOn
2 D. Cai, X. He, and J. Han, "Semi-supervised discriminant analysis," Proceedings of the international conference on computer vision, 2007.
3 G. Lim and C. H. Park, "Semi-supervised dimension reduction using graph-based discriminant analysis," Proceedings of the international conference on computer and information technology, 2009.
4 Y. Lee, Y. Shin, and C. H. Park, "Extending linear discriminant analysis by using unlabeled data," Proceedings of the international conference on computer and information technology, 2011.
5 K. Fukunaga, "Introduction to Statistical Pattern Recognition," second edition, Academic Press, 1990.
6 J. Wang, F. Wang, C. Zhang, H. Shen, and L. Quan, "Linear neighborhood propagation and its application," IEEE transactions on pattern analysis and machine intelligence, Vol.31, No.9, pp.1600-1615, 2009.   DOI   ScienceOn
7 G. H. Golub and C. F. Loan, "Matrix computations," 3rd edition, The Johns Hopkins University Press, 1996.
8 H. Kim, P. Holland and H. Park, "Dimension reduction in text classification with support vector machines," Journal of machine learning research, Vol.6, pp.37-53, 2005.
9 http://glaros.dtc.umn.edu/gkhome/cluto/cluto/download