Efficient Multiview Representation Learning With Correntropy and Anchor Graph
Nan Zhang, Xiaoqin Zhang, Shiliang Sun
Abstract
Graph-based multiview clustering methods have attracted much attention because of their ability to mine nonlinear structural information among instances. Although they perform well in many scenarios, they consume a lot of computational resources when dealing with large-scale multiview scenarios. To address this issue, we present a new insight into the anchor graph mechanism and propose a novel Nonnegative Anchor Graph Reconstruction (NAGR) model. NAGR introduces the sparse similarity graph into the symmetric matrix factorization and gets the nonnegative representation that retains the graph structural information. Thereafter, we develop a novel Efficient Multiview nonnegative Representation learning framework with Correntropy and Anchor graph (EMR-CA), which integrates multiview anchor graph reconstruction and consensus nonnegative representation learning into a unified framework. EMR-CA uses multiview anchor graph reconstruction to learn consensus nonnegative representation, where correntropy rather than F-norm is used as the approximation measurement criterion. Specifically, normalized anchor graphs of different views are decomposed into a consensus nonnegative representation and multiple view-specific representations, where the consensus representation retains the neighbor graph information between multiview instances and representative anchors on different views. Finally, the effectiveness of the proposed EMR-CA framework is verified by theoretical analysis and experimental results on large-scale realistic multiview scenarios.