Multi-view projected clustering with graph learning. (June 2020)

• Record Type: Journal Article
• Authors: Gao, Quanxue; Wan, Zhizhen; Liang, Ying; Wang, Qianqian; Liu, Yang; Shao, Ling
• Is Part Of: Neural networks. Volume 126(2020)
• Page Start: 335
• Page End: 346
• Publication Date: 2020-06
• Subjects: Multi-view -- Subspace learning -- Clustering -- Feature selection -- Local structure; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2020.03.020
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: Graph based multi-view learning is well known due to its effectiveness and good clustering performance. However, most existing methods directly construct graph from original high-dimensional data which always contain redundancy, noise and outlying entries in real applications, resulting in unreliable and inaccurate graph. Moreover, they do not effectively select some useful features which are important for graph learning and clustering. To solve these limits, we propose a novel model that combines dimensionality reduction, manifold structure learning and feature selection into a framework. We map high-dimensional data into low-dimensional space to reduce the complexity of the algorithm and reduce the effect of noise and redundance. Therefore, we can adaptively learn a more accurate graph. Further more, ℓ 21 -norm regularization is adopted to adaptively select some important features which help improve clustering performance. Finally, an efficiently algorithm is proposed to solve the optimal solution. Extensive experimental results on some benchmark datasets demonstrate the superiority of the proposed method.