Eigenvector selection in spectral clustering based on Bagging
Haijun Li · Journal of Shandong University · 2013
For the spectral clustering algorithm,the largest k eigenvectors of the affinity matrix derived from the dataset were not always able to find the structure of dataset effectively.An eigenvector selection algorithm in spectral clustering based on Bagging was proposed in order to select better eigenvectors.The eigenvectors were evaluated by pairwise constraints score.First,some eigenvectors were ranked according to their constraint scores,and then the suitable eigenvectors were selected from the ranking list,finally the optimal combination of k eigenvectors was obtained by Bagging-based ensemble algorithm.The better eigenvectors could be achieved.Experimental results on UCI benchmark datasets showed that this algorithm could gain satisfactory prediction results.