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

作者：

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.

摘要

•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.

论文关键词：Graph-based clustering,Constrained Laplacian rank,Extreme learning machine,Embedding,Graph Laplacian

论文评审过程：Received 11 March 2017, Revised 13 November 2017, Accepted 5 December 2017, Available online 8 December 2017, Version of Record 27 December 2017.

论文官网地址：https://doi.org/10.1016/j.patcog.2017.12.001