Ensemble Learning for Spectral Clustering

20 Nov 2020  ·  Hongmin Li, Xiucai Ye, Akira Imakura, Tetsuya Sakurai ·

Ensemble clustering has attracted much attention in machine learning and data mining for the high performance in the task of clustering. Spectral clustering is one of the most popular clustering methods and has superior performance compared with the traditional clustering methods. Existing ensemble clustering methods usually directly use the clustering results of the base clustering algorithms for ensemble learning, which cannot make good use of the intrinsic data structures explored by the graph Laplacians in spectral clustering, thus cannot obtain the desired clustering result. In this paper, we propose a new ensemble learning method for spectral clustering-based clustering algorithms. Instead of directly using the clustering results obtained from each base spectral clustering algorithm, the proposed method learns a robust presentation of graph Laplacian by ensemble learning from the spectral embedding of each base spectral clustering algorithm. Finally, the proposed method applies k-means on the spectral embedding obtain from the learned graph Laplacian to get clusters. Experimental results on both synthetic and real-world datasets show that the proposed method outperforms other existing ensemble clustering methods.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Image/Document Clustering australian ELSC Accuracy (%) 70.9 # 1
Image/Document Clustering BA ELSC Accuracy (%) 51.8 # 1
Image/Document Clustering iris ELSC Accuracy (%) 97.7 # 1
Image/Document Clustering JAFFE ELSC Accuracy (%) 98.6 # 1
Image/Document Clustering pixraw10P ELSC Accuracy (%) 96.0 # 1
Image/Document Clustering warpPIE10P ELSC Accuracy (%) 53.4 # 1
Image/Document Clustering Wine ELSC Accuracy (%) 75.8 # 1