Error Graph Regularized Nonnegative Matrix Factorization for Data Representation

作     者：Zhu, Qiang Zhou, Meijun Liu, Junping 

作者机构：Wuhan Text Univ Hubei Prov Engn Res Ctr Intelligent Text & Fash Sch Comp Sci & Artificial Intelligence Wuhan Peoples R China China Mobile Ltd Beijing Peoples R China 

出 版 物：《NEURAL PROCESSING LETTERS》 (神经处理通讯)

年 卷 期：2023年第55卷第6期

页      面：7321-7335页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Natural Science Foundation of China Team plan of scientific and technological innovation of outstanding youth in universities of Hubei province [T201807] Project of department of education of Hubei province [B2021099] 

主　　题：Error graph regularization Nonnegative matrix factorization Manifold learning Low-dimensional representation learning 

摘      要：Nonnegative matrix factorization (NMF) has been received much attention and widely applied to data mining by various researchers. It is believed that the non-negativity constraint makes NMF to learn a parts-based representation. Nevertheless, NMF fails to exploit the intrinsic manifold structure of the data. Therefore, many graph-based NMF methods have been proposed by incorporating a similarity graph. However, graph regularized NMF and its extensions do not consider the geometric structure of the given data is well preserved. In this paper, we propose an error graph regularized nonnegative matrix factorization (EGNMF) to perform the manifold learning. Our contribution is twofold: first, we introduce an error graph regularization term to maintain the geometric structures of the original data for each iterative update;second, we adopt a weight coefficient matrix to strengthen the important and weaken the non-important structures of the low-dimensional data. Experimental results on different benchmark datasets show that EGNMF is superior to competing methods.