Structured graph learning for clustering and semi-supervised classification

•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.