Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (21): 30-37.DOI: 10.3778/j.issn.1002-8331.1912-0199

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Subspace Clustering Induced by Adaptive Graph Learning

ZHU Dan, CHEN Xiaohong, WU Qingyuan, LI Shunming   

  1. 1.College of Science, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
    2.College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
  • Online:2020-11-01 Published:2020-11-03



  1. 1.南京航空航天大学 理学院,南京 211106
    2.南京航空航天大学 能源与动力学院,南京 211106


Subspace clustering is a hot research topic in machine learning. It aims at clustering data according to its potential subspace. Inspired by the collaborative training algorithm in multi-view learning, a subspace clustering algorithm induced by adaptive graph learning is proposed. The single-view data is multi-viewed by two different ways. Regularization term of the graph is iteratively updated according to the information of different views. Block diagonal affinity matrix is obtained which better reflect the clustering performance, so as to describe the data clustering results more accurately. Compared with other clustering algorithms on four standard datasets, the experimental results show the better clustering performance of the proposed method.

Key words: collaborative training, spectral clustering, subspace clustering, affinity matrix



关键词: 协同训练, 谱聚类, 子空间聚类, 关联矩阵