Learning a Subspace and Clustering Simultaneously with Manifold Regularized Nonnegative Matrix Factorization  

作　　者：Feiping Nie Huimin Chen Heng Huang Chris H.Q.Ding Xuelong Li 

机构地区：[1]School of Computer Science Northwestern Polytechnical University Xi'an 710072,P.R.China [2]School of Artificial Intelligence Optics and Electronics(iOPEN)Northwestern Polytechnical University Xi'an 710072,P.R.China [3]Key Laboratory of Intelligent Interaction and Applications(Northwestern Polytechnical University)Ministry of Industry and Information Technology Xi'an 710072,P.R.China [4]Department of Electrical and Computer Engineering University of Pittsburgh,Pittsburgh,PA 15260,USA [5]Department of Computer Science and Engineering The Chinese University of Hong Kong Shenzhen 518172,P.R.China [6]Institute of Artificial Intelligence(TeleAI)China Telecom Corporation Limited 31 Jinrong Street,Beijing 100033,P.R.China

出　　处：《Guidance, Navigation and Control》2024年第3期147-165,共19页制导、导航与控制(英文)

摘　　要：With the incredible growth of high-dimensional data such as microarray gene expression data and web blogs from internet, the researchers are desirable to develop new clustering techniques to address the critical problem created by irrelevant dimensions. Properties of Nonnegative Matrix Factorization(NMF) as a clustering method were studied by relating its formulation to other methods such as K-means clustering. In this paper, by introducing clustering indicator constraints on NMF and incorporating manifold regularization to preserve geometric structures,we propose a novel manifold regularized NMF method that can simultaneously learn subspace and do clustering. As a result, our clustering results can directly assign cluster label to data points. Extensive experimental results show that our method outperforms related other methods.

关 键 词：Subspace learning CLUSTERING nonnegative matrix factorization manifold learning 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论] TP18[自动化与计算机技术—计算机科学与技术]