中文版 | English
Title

HyperNTF: A hypergraph regularized nonnegative tensor factorization for dimensionality reduction

Author
Corresponding AuthorLiu,Quanying
Publication Years
2022-11-01
DOI
Source Title
ISSN
0925-2312
EISSN
1872-8286
Volume512Pages:190-202
Abstract
Tensor decomposition is an effective tool for learning multi-way structures and heterogeneous features from high-dimensional data, such as the multi-view images and multichannel electroencephalography (EEG) signals, are often represented by tensors. However, most of tensor decomposition methods are the linear feature extraction techniques, which are unable to reveal the nonlinear structure within high-dimensional data. To address such problem, a lot of algorithms have been proposed for simultaneously performs linear and non-linear feature extraction. A representative algorithm is the Graph Regularized Nonnegative Matrix Factorization (GNMF) for image clustering. However, the normal 2-order graph can only model the pairwise similarity of objects, which cannot sufficiently exploit the complex structures of samples. Thus, we propose a novel method, named Hypergraph Regularized Nonnegative Tensor Factorization (HyperNTF), which utilizes hypergraph to model the complex connections among samples and employs the factor matrix corresponding with last mode of Canonical Polyadic (CP) decomposition as low-dimensional representation of original data. Extensive experiments on synthetic manifolds, real-world image datasets, and EEG signals, demonstrating that HyperNTF outperforms the state-of-the-art methods in terms of dimensionality reduction, clustering, and classification.
Keywords
URL[Source Record]
Indexed By
Language
English
SUSTech Authorship
First ; Corresponding
Funding Project
National Key Research and Development Program of China[2021YFF1200804] ; National Natural Science Foundation of China[62001205] ; Guangdong Natural Science Foundation Joint Fund[2019A1515111038] ; Shenzhen Science and Technology Innovation Committee["20200925155957004","KCXFZ2020122117340001"] ; Shenzhen-Hong Kong-Macao Science and Technology Innovation Project[SGDX2020110309280100] ; Shenzhen Key Laboratory of Smart Healthcare Engineering[ZDSYS20200811144003009]
WOS Research Area
Computer Science
WOS Subject
Computer Science, Artificial Intelligence
WOS Accession No
WOS:000862469300014
Publisher
ESI Research Field
COMPUTER SCIENCE
Scopus EID
2-s2.0-85138396650
Data Source
Scopus
Citation statistics
Cited Times [WOS]:1
Document TypeJournal Article
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/402662
DepartmentDepartment of Biomedical Engineering
Affiliation
1.Shenzhen Key Laboratory of Smart Healthcare Engineering,Department of Biomedical Engineering,Southern University of Science and Technology,Shenzhen,Guangdong,518055,China
2.School of Electronics and Information Technology,Sun Yat-sen University,Guangzhou,Guangdong,510006,China
First Author AffilicationDepartment of Biomedical Engineering
Corresponding Author AffilicationDepartment of Biomedical Engineering
First Author's First AffilicationDepartment of Biomedical Engineering
Recommended Citation
GB/T 7714
Yin,Wanguang,Qu,Youzhi,Ma,Zhengming,et al. HyperNTF: A hypergraph regularized nonnegative tensor factorization for dimensionality reduction[J]. NEUROCOMPUTING,2022,512:190-202.
APA
Yin,Wanguang,Qu,Youzhi,Ma,Zhengming,&Liu,Quanying.(2022).HyperNTF: A hypergraph regularized nonnegative tensor factorization for dimensionality reduction.NEUROCOMPUTING,512,190-202.
MLA
Yin,Wanguang,et al."HyperNTF: A hypergraph regularized nonnegative tensor factorization for dimensionality reduction".NEUROCOMPUTING 512(2022):190-202.
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