Correntropy-Based Hypergraph Regularized NMF for Clustering and Feature Selection on Multi-Cancer Integrated Data.

Journal: IEEE transactions on cybernetics
Published Date:

Abstract

Non-negative matrix factorization (NMF) has become one of the most powerful methods for clustering and feature selection. However, the performance of the traditional NMF method severely degrades when the data contain noises and outliers or the manifold structure of the data is not taken into account. In this article, a novel method called correntropy-based hypergraph regularized NMF (CHNMF) is proposed to solve the above problem. Specifically, we use the correntropy instead of the Euclidean norm in the loss term of CHNMF, which will improve the robustness of the algorithm. And the hypergraph regularization term is also applied to the objective function, which can explore the high-order geometric information in more sample points. Then, the half-quadratic (HQ) optimization technique is adopted to solve the complex optimization problem of CHNMF. Finally, extensive experimental results on multi-cancer integrated data indicate that the proposed CHNMF method is superior to other state-of-the-art methods for clustering and feature selection.

Authors

  • Na Yu
    National Dental Centre Singapore, Singapore.
  • Ming-Juan Wu
  • Jin-Xing Liu
    School of Information Science and Engineering, Qufu Normal University, Rizhao, China; Co-Innovation Center for Information Supply & Assurance Technology, Anhui University, Hefei, China. Electronic address: sdcavell@126.com.
  • Chun-Hou Zheng
  • Yong Xu
    Department of Psychiatry, The First Hospital of Shanxi Medical University, Taiyuan, China.