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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- Korean Institute of Information Scientists and Engineers Journal of Computing Science and Engineering Journal of Computing Science and Engineering Vol.11 No.4
- 발행연도
- 2017.12
- 수록면
- 121 - 129 (9page)
- DOI
- 10.5626/JCSE.2017.11.4.121
이용수
초록· 키워드
오류제보하기
Feature selection has been widely established as an efficient technique for microarray data analysis. Feature selection aims to search for the most important feature/gene subset of a given dataset according to its relevance to the current target. Unsupervised feature selection is considered to be challenging due to the lack of label information. In this paper, we propose a novel method for unsupervised feature selection, which incorporates embedded learning and l<SUB>2,1</SUB>-norm sparse regression into a framework to select genes in microarray data analysis. Local tangent space alignment is applied during embedded learning to preserve the local data structure. The l<SUB>2,1</SUB>-norm sparse regression acts as a constraint to aid in learning the gene weights correlatively, by which the proposed method optimizes for selecting the informative genes which better capture the interesting natural classes of samples. We provide an effective algorithm to solve the optimization problem in our method. Finally, to validate the efficacy of the proposed method, we evaluate the proposed method on real microarray gene expression datasets. The experimental results demonstrate that the proposed method obtains quite promising performance.
목차
Abstract
I. INTRODUCTION
II. PRELIMINARIES
III. THE PROPOSED METHOD
IV. EXPERIMENTS
V. CONCLUSION
REFERENCES
참고문헌 (17)
참고문헌 신청
E. De Rinaldis and A. Lahm, DNA Microarrays: Current Applications. Norfolk, UK: Horizon Scientific Press, 2007.
T. R. Golub, D. K. Slonim, P. Tamayo, C. Huard, M. Gaasenbeek, J. P. Mesirov, H. Coller, M. L. Loh, J. R. Downing, M. A. Caligiuri, et al., “Molecular classification of cancer: class discovery and class prediction by gene expression monitoring,” Science, vol. 286, no. 5439, pp. 531- 537, 1999.
R. L. Somorjai, B. Dolenko, and R. Baumgartner, “Class prediction and discovery using gene microarray and proteomics mass spectroscopy data: curses, caveats, cautions,” Bioinformatics, vol. 19, no. 12, pp. 1484-1491, 2003.
I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, “Gene selection for cancer classification using support vector machines,” Machine Learning, vol. 46, no. 1, pp. 389-422, 2002.
J. G. Dy and C. E. Brodley, “Visualization and interactive feature selection for unsupervised data,” in Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Boston, MA, 2000, pp. 360-364.
T. R. Golub, D. K. Slonim, P. Tamayo, C. Huard, M. Gaasenbeek, J. P. Mesirov, H. Coller, M. L. Loh, J. R. Downing, M. A. Caligiuri, et al., “Molecular classification of cancer: class discovery and class prediction by gene expression monitoring,” Science, vol. 286, no. 5439, pp. 531- 537, 1999.
R. L. Somorjai, B. Dolenko, and R. Baumgartner, “Class prediction and discovery using gene microarray and proteomics mass spectroscopy data: curses, caveats, cautions,” Bioinformatics, vol. 19, no. 12, pp. 1484-1491, 2003.
I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, “Gene selection for cancer classification using support vector machines,” Machine Learning, vol. 46, no. 1, pp. 389-422, 2002.
J. G. Dy and C. E. Brodley, “Visualization and interactive feature selection for unsupervised data,” in Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Boston, MA, 2000, pp. 360-364.
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UCI(KEPA) : I410-ECN-0101-2018-569-001718557