HyperNTF: A hypergraph regularized nonnegative tensor factorization for dimensionality reduction

Yin，Wanguang1; Qu，Youzhi1; Ma，Zhengming2; Liu，Quanying1

2022-11-01

10.1016/j.neucom.2022.09.036

NEUROCOMPUTING   影响因子和分区

0925-2312

1872-8286

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.

Classification Clustering Dimension reduction EEG Hypergraph Nonnegative Tensor Factorization (NTF)

SCI

英语

第一 ; 通讯

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]

Computer Science

Computer Science, Artificial Intelligence

WOS:000862469300014

ELSEVIER

COMPUTER SCIENCE

2-s2.0-85138396650

Scopus

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

Yin，Wanguang,Qu，Youzhi,Ma，Zhengming,et al. HyperNTF: A hypergraph regularized nonnegative tensor factorization for dimensionality reduction[J]. NEUROCOMPUTING,2022,512:190-202.

Yin，Wanguang,Qu，Youzhi,Ma，Zhengming,&Liu，Quanying.(2022).HyperNTF: A hypergraph regularized nonnegative tensor factorization for dimensionality reduction.NEUROCOMPUTING,512,190-202.

Yin，Wanguang,et al."HyperNTF: A hypergraph regularized nonnegative tensor factorization for dimensionality reduction".NEUROCOMPUTING 512(2022):190-202.