Unsupervised Ensemble Learning Based on Graph Embedding for Image Clustering

  • Xiaohui Luo
  • Li Zhang
  • Fanzhang Li
  • Chengxiang Hu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11303)


Manifold learning has attracted more and more attention in machine learning for past decades. Unsupervised Large Graph Embedding (ULGE), which performs well on the large-scale data, has been proposed for manifold learning. To improve the clustering performance, a novel Unsupervised Ensemble Learning based on Graph Embedding (UEL-GE) is explored, which takes ULGE to get low-dimensional embeddings of the given data and uses the K-means method to obtain the clustering results. Furthermore, the multiple clusterings are corrected by using the bestMap method. Finally, the corrected clusterings are combined to generate the final clustering. Extensive experiments on several data sets are conducted to show the efficiency and effectiveness of the proposed ensemble learning method.


Ensemble learning Image clustering Dimension reduction Manifold learning 



This work was supported in part by the National Natural Science Foundation of China under Grant Nos. 61373093, 61402310, 61672364 and 61672365, by the Soochow Scholar Project of Soochow University, by the Six Talent Peak Project of Jiangsu Province of China and by the Graduate Innovation and Practice Program of colleges and universities in Jiangsu Province.


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Xiaohui Luo
    • 1
  • Li Zhang
    • 1
  • Fanzhang Li
    • 1
  • Chengxiang Hu
    • 1
  1. 1.School of Computer Science and Technology and Joint International Research Laboratory of Machine Learning and Neuromorphic Computing, Provincial Key Laboratory for Computer Information Processing TechnologySoochow UniversitySuzhouChina

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