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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2017.11
- 수록면
- 1,271 - 1,278 (8page)
이용수
초록· 키워드
오류제보하기
Although the L₀-penalty is the most natural choice to identify the sparsity structure of the model, it has not been widely used due to the computational bottleneck. Recently, the adaptive ridge procedure is developed to effciently approximate a L<SUB>q</SUB>-penalized problem to an iterative L₂-penalized one. In this article, we proposed to apply the adaptive ridge procedure to solve the L₀-penalized weighted support vector machine (WSVM) to facilitate the corresponding optimization. Our numerical investigation shows the advantageous performance of the L₀-penalized WSVM compared to the conventional WSVM with L₂ penalty for both simulated and real data sets.
목차
Abstract
1. Introduction
2. L₀-penalized weighted support vector machine
3. Adaptive ridge procedure
4. Numerical illustration
5. Conclusion
References
참고문헌 (15)
참고문헌 신청
Amaldi, E. and Kann, V. (1998). On the approximability of minimizing nonzero variables or unsatis ed relations in linear systems. Theoretical Computer Science, 209, 237-260.
Fan, J. and Li, R. (2001). Variable selection via nonconcave penalized likelihood and its oracle properties. Journal of the American statistical Association, 96, 1348-1360.
Frommlet, F. and Nuel, G. (2016). An adaptive ridge procedure for l0 regularization. PloS One, 11, e0148620.
Fung, G. M., Mangasarian, O. L. and Smola, A. J. (2002). Minimal kernel classifiers. Journal of Machine Learning Research, 3, 303-321.
Huang, K., King, I. and Lyu, M. R. (2008). Direct zero-norm optimization for feature selection. Eighth IEEE International Conference, 845-850.
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