Structured graph learning for clustering and semi-supervised classification

作者：

Highlights：

• A graph learning framework, which captures both the global and local structure in data, is proposed.

• Theoretical analysis builds the connections of our model to k-means, spectral clustering, and kernel k-means.

• Extensions to semi-supervised classification and multiple kernel learning are presented.

摘要

•A graph learning framework, which captures both the global and local structure in data, is proposed.•Theoretical analysis builds the connections of our model to k-means, spectral clustering, and kernel k-means.•Extensions to semi-supervised classification and multiple kernel learning are presented.

论文关键词：Similarity graph,Rank constraint,Clustering,Semi-supervised classification,Local ang global structure,Kernel method

论文评审过程：Received 19 February 2020, Revised 29 May 2020, Accepted 30 August 2020, Available online 2 September 2020, Version of Record 6 September 2020.

论文官网地址：https://doi.org/10.1016/j.patcog.2020.107627