원문정보
보안공학연구지원센터(IJSIP)
International Journal of Signal Processing, Image Processing and Pattern Recognition
Vol.4 No.3
2011.09
pp.179-194
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
Spectral clustering is a powerful technique in clustering specially when the structure of data is not linear and classical clustering methods lead to fail. In this paper, we propose a spectral clustering algorithm with a feature selection schema based on extracted features of Kernel PCA. In the proposed algorithm, selecting appropriate vectors is dependent upon entropy of clusters on these vectors and weighting method is influenced by sum of the existence gap between clusters and entropy of the vectors. Tuning the parameters has a great effect on the results of spectral clustering techniques. In the ideal case, comparing our method with NJW and Kernel K-Means indicate the successful of the proposed algorithm.
목차
Abstract
1. Introduction
2. Preliminaries
2.1. Spectral Clustering
2.2. Kernel Clustering Methods
2.3. Kernel PCA and Weighted Kernel PCA
3. Proposed Method
3.1. The Importance of Eigenvector Selection
3.2. Eigenvector Selection and Weighting Schema
3.3. Proposed Algorithm
3.4. Parameter Selection
3.5. Out-of-sample Extension
4.Experimental Results
4.1.Toy Problems
4.2.Experiments on UCI benchmark datasets
4.3.Robustness Analysis
5. Conclusion
References
1. Introduction
2. Preliminaries
2.1. Spectral Clustering
2.2. Kernel Clustering Methods
2.3. Kernel PCA and Weighted Kernel PCA
3. Proposed Method
3.1. The Importance of Eigenvector Selection
3.2. Eigenvector Selection and Weighting Schema
3.3. Proposed Algorithm
3.4. Parameter Selection
3.5. Out-of-sample Extension
4.Experimental Results
4.1.Toy Problems
4.2.Experiments on UCI benchmark datasets
4.3.Robustness Analysis
5. Conclusion
References
저자정보
참고문헌
자료제공 : 네이버학술정보