An adaptive graph learning method based on dual data representations for clustering

Tianchi Liu, Chamara Kasun Liyanaarachchi Lekamalage, Guang-Bin Huang, Zhiping Lin

Abstract: Highlights•Showing that combining original data with a proper nonlinear embedding could be a better basis for adaptive graph learning.•Development of dual representations, i.e., the original data and a nonlinear embedding obtained by an Extreme Learning Machine-based neural network.•Proposing a novel adaptive graph learning method for clustering based on the dual representation.•Extensive experiments on both synthetic and real-world benchmark datasets verified the effectiveness of the proposed method.