Feature Nonlinear Transformation Non-Negative Matrix Factorization with Kullback-Leibler Divergence

作     者：Hu, Lirui Wu, Ning Li, Xiao 

作者机构：Beibu Gulf Univ Coll Elect & Informat Engn Qinzhou 535000 Guangxi Peoples R China Key Lab Big Data Resources Utilizat Qinzhou 535000 Guangxi Peoples R China Key Lab Adv Technol Internet Things Qinzhou 535000 Guangxi Peoples R China Guilin Univ Technol Coll Informat Sci & Engn Guilin 541000 Guangxi Peoples R China 

出 版 物：《PATTERN RECOGNITION》 (图形识别)

年 卷 期：2022年第132卷

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Science and Technology Development Program of Chinese Guangxi Province [AC17195057] Natural Science Foundation of Chinese Guangxi Province [2016GXNSFAA380102] High-level Scientific Research and Cultivation Project of Qinzhou University, China [2016PY-GJ03] Key Laboratory of Advanced Technology for Internet of Things, Qinzhou, China [IOT2017A02] School Grade Research Project of Qinzhou University, China [2017KYQD118] 

主　　题：Non -negative matrix factorization Nonlinear transformation Feature extraction Object recognition Clustering Kullback-Leibler divergence 

摘      要：This paper introduces a Feature Nonlinear Transformation Non-Negative Matrix Factorization with Kullback-Leibler Divergence (FNTNMF-KLD) for extracting the nonlinear features of a matrix in standard NMF. This method uses a nonlinear transformation to act on the feature matrix for constructing a NMF model based on the objective function of Kullback-Leibler Divergence, and the Taylor series expansion and the Newton iteration formula of solving root are used to obtain the iterative update rules of the basis ma-trix and the feature matrix. Experimental results show that the proposed method obtains the nonlinear features of data matrix in a more efficient way. In object recognition and clustering tasks, better accuracy can be achieved over some typical NMF methods.(c) 2022 Elsevier Ltd. All rights reserved.