Multiple graph adaptive regularized semi-supervised nonnegative matrix factorization with sparse constraint for data representation

HIGHLIGHTS

• who: Kexin Zhang and collaborators from the School of Intelligent Engineering, Zhengzhou University of Aeronautics, Zhengzhou, China have published the research: Multiple Graph Adaptive Regularized Semi-Supervised Nonnegative Matrix Factorization with Sparse Constraint for Data Representation, in the : Processes 2022, 10, 2623. of /2022/
• what: In this paper, a new sparse approach, namely, the multiple graph adaptive regularized SSNMF with sparse constraint (MSNMFSC), is proposed for exploiting good data representation. The authors combine the multiple graph adaptive regularization, the limited supervised information and the sparse constraint together, then propose the MSNMFSC algorithm accordingly.
•  

  

  If you want to have access to all the content you need to log in!

  Thanks :)

  Username or Email

  Password

  Remember me

  Lost your password?

  If you don't have an account, you can create one here.