关键词: 谱聚类, Bagging方法, 特征向量选择, 拉普拉斯矩阵, 约束计分

Abstract:

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.

Key words: eigenvector selection, spectral clustering, constraint score, Laplacian matrix, Bagging method