Sparse Feature-Weighted Double Laplacian Rank Constraint Non-Negative Matrix Factorization for Image Clustering

Sparse Feature-Weighted Double Laplacian Rank Constraint Non-Negative Matrix Factorization for Image Clustering

As an extension of non-negative matrix factorization (NMF), graph-regularized non-negative matrix factorization (GNMF) has been widely applied in data mining and machine learning, particularly for tasks such as clustering and feature selection. Traditional GNMF methods typically rely on predefined g...

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Bibliographic Details
Main Authors: Hu Ma, Ziping Ma, Huirong Li, Jingyu Wang
Format: Article
Language:English
Published: MDPI AG 2024-11-01
Series:Mathematics
Subjects:
non-negative matrix factorization
local structure learning
feature weighting
sparse constraints
constrained Laplacian rank
Online Access:https://www.mdpi.com/2227-7390/12/23/3656

Internet

https://www.mdpi.com/2227-7390/12/23/3656

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