期刊文献+
任意字段
题名或关键词
题名
关键词
摘要
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息
共找到24篇文章
< 1 2 >
每页显示 20 50 100
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
显示方式：
Extracting Sub-Networks from Brain Functional Network Using Graph Regularized Nonnegative Matrix Factorization 认领 引用 被引量：1
1
作者 Zhuqing Jiao Yixin Ji +1 位作者 Tingxuan Jiao Shuihua Wang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2020年第5期845-871,共27页
Currently,functional connectomes constructed from neuroimaging data have emerged as a powerful tool in identifying brain disorders.If one brain disease just manifests as some cognitive dysfunction,it means that the di... Currently,functional connectomes constructed from neuroimaging data have emerged as a powerful tool in identifying brain disorders.If one brain disease just manifests as some cognitive dysfunction,it means that the disease may affect some local connectivity in the brain functional network.That is,there are functional abnormalities in the sub-network.Therefore,it is crucial to accurately identify them in pathological diagnosis.To solve these problems,we proposed a sub-network extraction method based on graph regularization nonnegative matrix factorization(GNMF).The dynamic functional networks of normal subjects and early mild cognitive impairment(eMCI)subjects were vectorized and the functional connection vectors(FCV)were assembled to aggregation matrices.Then GNMF was applied to factorize the aggregation matrix to get the base matrix,in which the column vectors were restored to a common sub-network and a distinctive sub-network,and visualization and statistical analysis were conducted on the two sub-networks,respectively.Experimental results demonstrated that,compared with other matrix factorization methods,the proposed method can more obviously reflect the similarity between the common subnetwork of eMCI subjects and normal subjects,as well as the difference between the distinctive sub-network of eMCI subjects and normal subjects,Therefore,the high-dimensional features in brain functional networks can be best represented locally in the lowdimensional space,which provides a new idea for studying brain functional connectomes. 展开更多
关键词 Brain functional network sub-network functional connectivity graph regularized nonnegative matrix factorization(GNMF) aggregation matrix
暂未订购 下载PDF
Minimum distance constrained nonnegative matrix factorization for hyperspectral data unmixing 认领 引用 被引量：2
2
作者 于钺 SunWeidong 《High Technology Letters》 EI CAS 2012年第4期333-342,共10页
This paper considers a problem of unsupervised spectral unmixing of hyperspectral data. Based on the Linear Mixing Model （ LMM）, a new method under the framework of nonnegative matrix fac- torization （NMF） is prop... This paper considers a problem of unsupervised spectral unmixing of hyperspectral data. Based on the Linear Mixing Model （ LMM）, a new method under the framework of nonnegative matrix fac- torization （NMF） is proposed, namely minimum distance constrained nonnegative matrix factoriza- tion （MDC-NMF）. In this paper, firstly, a new regularization term, called endmember distance （ED） is considered, which is defined as the sum of the squared Euclidean distances from each end- member to their geometric center. Compared with the simplex volume, ED has better optimization properties and is conceptually intuitive. Secondly, a projected gradient （PG） scheme is adopted, and by the virtue of ED, in this scheme the optimal step size along the feasible descent direction can be calculated easily at each iteration. Thirdly, a finite step （ no more than the number of endmem- bers） terminated algorithm is used to project a point on the canonical simplex, by which the abun- dance nonnegative constraint and abundance sum-to-one constraint can be accurately satisfied in a light amount of computation. The experimental results, based on a set of synthetic data and real da- ta, demonstrate that, in the same running time, MDC-NMF outperforms several other similar meth- ods proposed recently. 展开更多
关键词 hyperspectral data nonnegative matrix factorization （NMF） spectral unmixing convex function projected gradient （PG）
暂未订购 下载PDF
Image Fusion Based on Complex Contourlet Transform and Nonnegative Matrix Factorization 认领 引用 被引量：1
3
作者 吴一全 侯雯 吴诗婳 《Transactions of Tianjin University》 EI CAS 2012年第4期266-270,共5页
An image fusion method combining complex contourlet transform(CCT) with nonnegative matrix factorization(NMF) is proposed in this paper.After two images are decomposed by CCT,NMF is applied to their highand low-freque... An image fusion method combining complex contourlet transform(CCT) with nonnegative matrix factorization(NMF) is proposed in this paper.After two images are decomposed by CCT,NMF is applied to their highand low-frequency components,respectively,and finally an image is synthesized.Subjective-visual-quality of the image fusion result is compared with those of the image fusion methods based on NMF and the combination of wavelet /contourlet onsubsampled contourlet with NMF.The experimental results are evaluated quantitatively,and the running time is also contrasted.It is shown that the proposed image fusion method can gain larger information entropy,standard deviation and mean gradient,which means that it can better integrate featured information from all source images,avoid background noise and promote space clearness in the fusion image effectively. 展开更多
关键词 image fusion complex contourlet transform nonnegative matrix factorization
暂未订购 下载PDF
Cold-Start Link Prediction via Weighted Symmetric Nonnegative Matrix Factorization with Graph Regularization 认领 引用
4
作者 Minghu Tang Wei Yu +3 位作者 Xiaoming Li Xue Chen Wenjun Wang Zhen Liu 《Computer Systems Science & Engineering》 SCIE EI 2022年第12期1069-1084,共16页
Link prediction has attracted wide attention among interdisciplinaryresearchers as an important issue in complex network. It aims to predict the missing links in current networks and new links that will appear in futu... Link prediction has attracted wide attention among interdisciplinaryresearchers as an important issue in complex network. It aims to predict the missing links in current networks and new links that will appear in future networks.Despite the presence of missing links in the target network of link prediction studies, the network it processes remains macroscopically as a large connectedgraph. However, the complexity of the real world makes the complex networksabstracted from real systems often contain many isolated nodes. This phenomenon leads to existing link prediction methods not to efficiently implement the prediction of missing edges on isolated nodes. Therefore, the cold-start linkprediction is favored as one of the most valuable subproblems of traditional linkprediction. However, due to the loss of many links in the observation network, thetopological information available for completing the link prediction task is extremely scarce. This presents a severe challenge for the study of cold-start link prediction. Therefore, how to mine and fuse more available non-topologicalinformation from observed network becomes the key point to solve the problemof cold-start link prediction. In this paper, we propose a framework for solving thecold-start link prediction problem, a joint-weighted symmetric nonnegative matrixfactorization model fusing graph regularization information, based on low-rankapproximation algorithms in the field of machine learning. First, the nonlinear features in high-dimensional space of node attributes are captured by the designedgraph regularization term. Second, using a weighted matrix, we associate the attribute similarity and first order structure information of nodes and constrain eachother. Finally, a unified framework for implementing cold-start link prediction isconstructed by using a symmetric nonnegative matrix factorization model to integrate the multiple information extracted together. Extensive experimental validationon five real networks with attributes shows that the proposed model has very goodpredictive performance when predicting missing edges of isolated nodes. 展开更多
关键词 Link prediction cold-start nonnegative matrix factorization graph regularization
暂未订购 下载PDF
Symmetric Nonnegative Matrix Factorization for Vertex Centrality in Complex Networks 认领 引用
5
作者 LU Pengli CHEN Wei +1 位作者 GUO Yuhong CHEN Yahong 《Journal of Shanghai Jiaotong university(Science)》 EI 2024年第6期1037-1049,共13页
One of the most important problems in complex networks is to identify the influential vertices for understanding and controlling of information diffusion and disease spreading.Most of the current centrality algorithms... One of the most important problems in complex networks is to identify the influential vertices for understanding and controlling of information diffusion and disease spreading.Most of the current centrality algorithms focus on single feature or manually extract the attributes,which occasionally results in the failure to fully capture the vertex’s importance.A new vertex centrality approach based on symmetric nonnegative matrix factorization(SNMF),called VCSNMF,is proposed in this paper.For highlight the characteristics of a network,the adjacency matrix and the degree matrix are fused to represent original data of the network via a weighted linear combination.First,SNMF automatically extracts the latent characteristics of vertices by factorizing the established original data matrix.Then we prove that each vertex’s composite feature which is constructed with one-dimensional factor matrix can be approximated as the term of eigenvector associated with the spectral radius of the network,otherwise obtained by the factor matrix on the hyperspace.Finally,VCSNMF integrates the composite feature and the topological structure to evaluate the performance of vertices.To verify the effectiveness of the VCSNMF criterion,eight existing centrality approaches are used as comparison measures to rank influential vertices in ten real-world networks.The experimental results assert the superiority of the method. 展开更多
关键词 complex networks centrality symmetric nonnegative matrix factorization(SNMF)
暂未订购 下载PDF
Vertex centrality of complex networks based on joint nonnegative matrix factorization and graph embedding 认领 引用
6
作者 卢鹏丽 陈玮 《Chinese Physics B》 SCIE EI CAS CSCD 2023年第1期634-645,共12页
Finding crucial vertices is a key problem for improving the reliability and ensuring the effective operation of networks,solved by approaches based on multiple attribute decision that suffer from ignoring the correlat... Finding crucial vertices is a key problem for improving the reliability and ensuring the effective operation of networks,solved by approaches based on multiple attribute decision that suffer from ignoring the correlation among each attribute or the heterogeneity between attribute and structure. To overcome these problems, a novel vertex centrality approach, called VCJG, is proposed based on joint nonnegative matrix factorization and graph embedding. The potential attributes with linearly independent and the structure information are captured automatically in light of nonnegative matrix factorization for factorizing the weighted adjacent matrix and the structure matrix, which is generated by graph embedding. And the smoothness strategy is applied to eliminate the heterogeneity between attributes and structure by joint nonnegative matrix factorization. Then VCJG integrates the above steps to formulate an overall objective function, and obtain the ultimately potential attributes fused the structure information of network through optimizing the objective function. Finally, the attributes are combined with neighborhood rules to evaluate vertex's importance. Through comparative analyses with experiments on nine real-world networks, we demonstrate that the proposed approach outperforms nine state-of-the-art algorithms for identification of vital vertices with respect to correlation, monotonicity and accuracy of top-10 vertices ranking. 展开更多
关键词 complex networks centrality joint nonnegative matrix factorization graph embedding smoothness strategy
暂未订购 下载PDF
Orthogonal nonnegative matrix factorization based local hidden Markov model for multimode process monitoring 认领 引用 被引量：3
7
作者 Fan Wang Honglin Zhu +1 位作者 Shuai Tan Hongbo Shi 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2016年第7期856-860,共5页
Traditional data driven fault detection methods assume that the process operates in a single mode so that they cannot perform well in processes with multiple operating modes. To monitor multimode processes effectively... Traditional data driven fault detection methods assume that the process operates in a single mode so that they cannot perform well in processes with multiple operating modes. To monitor multimode processes effectively,this paper proposes a novel process monitoring scheme based on orthogonal nonnegative matrix factorization(ONMF) and hidden Markov model(HMM). The new clustering technique ONMF is employed to separate data from different process modes. The multiple HMMs for various operating modes lead to higher modeling accuracy.The proposed approach does not presume the distribution of data in each mode because the process uncertainty and dynamics can be well interpreted through the hidden Markov estimation. The HMM-based monitoring indication named negative log likelihood probability is utilized for fault detection. In order to assess the proposed monitoring strategy, a numerical example and the Tennessee Eastman process are used. The results demonstrate that this method provides efficient fault detection performance. 展开更多
关键词 Multimode processFault detectionHidden Markov modelOrthogonal nonnegative matrix factorization
暂未订购 下载PDF
Robust azimuth ambiguity detection method in SAR images based on non-negative matrix factorization 认领 引用
8
作者 LI Jieshuang TAO Mingliang +2 位作者 CUI Lei LIU Yanyang WANG Ling 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2026年第3期743-754,共12页
Azimuth ambiguity significantly degrades the quality of synthetic aperture radar images.Sub-look spectral analysis(SSA)is a common ambiguity-detection method,but its performance is limited by threshold sensitivity and... Azimuth ambiguity significantly degrades the quality of synthetic aperture radar images.Sub-look spectral analysis(SSA)is a common ambiguity-detection method,but its performance is limited by threshold sensitivity and the high correlation of specific ambiguities across sub-looks.To overcome these specific limitations,this paper proposes an improved detection method.It first increases the number of sub-looks and constructs a high-dimensional multi-look matrix to enrich the coherence differences between targets and ambiguities.Non-negative matrix factorization is then employed to decompose this matrix,effectively separating the coherent target components from the variably coherent ambiguity components without relying on predefined thresholds.Experimental results on real data demonstrate that the proposed improvements achieve superior azimuth-ambiguity-detection performance compared with conventional SSA methods. 展开更多
关键词 synthetic aperture radar(SAR) azimuth ambiguity multi-look processing nonnegative matrix factorization(NMF)
暂未订购 下载PDF
Identifying spatiotemporal traffic patterns in large-scale urban road networks using a modified nonnegative matrix factorization algorithm 认领 引用 被引量：2
9
作者 Xiaolei Ma Yi Li Peng Chen 《Journal of Traffic and Transportation Engineering(English Edition)》 EI CSCD 2020年第4期529-539,共11页
The identification and analysis of spatiotemporal traffic patterns in road networks constitute a crucial process for sophisticated traffic management and control.Traditional methods based on mathematical equations and... The identification and analysis of spatiotemporal traffic patterns in road networks constitute a crucial process for sophisticated traffic management and control.Traditional methods based on mathematical equations and statistical models can hardly be applicable to large-scale urban road networks,where traffic states exhibit high degrees of dynamics and complexity.Recently,advances in data collection and processing have provided new opportunities to effectively understand spatiotemporal traffic patterns in large-scale road networks using data-driven methods.However,limited efforts have been exerted to explore the essential structure of the networks when conducting a spatiotemporal analysis of traffic characteristics.To this end,this study proposes a modified nonnegative matrix factorization algorithm that processes high-dimensional traffic data and provides an improved representation of the global traffic state.After matrix factorization,cluster analysis is conducted based on the obtained low-dimensional representative matrices,which contain different traffic patterns and serve as the basis for exploring the temporal dynamics and spatial structure of network congestion.The applicability and effectiveness of the proposed approach are examined in a road network of Beijing,China.Results show that the methods exhibit considerable potential for identifying and interpreting the spatiotemporal traffic patterns over the entire network and provide a systematic and efficient approach for analyzing the network-level traffic state. 展开更多
关键词 Large-scale network Traffic state Spatiotemporal patterns Nonnegative matrix factorization
暂未订购 下载PDF
Clustering-based hyperspectral band selection using sparse nonnegative matrix factorization 认领 引用 被引量：14
10
作者 Ji-ming LI Yun-tao QIAN 《Journal of Zhejiang University-Science C(Computers and Electronics)》 2011年第7期542-549,共8页
Hyperspectral imagery generally contains a very large amount of data due to hundreds of spectral bands.Band selection is often applied firstly to reduce computational cost and facilitate subsequent tasks such as land-... Hyperspectral imagery generally contains a very large amount of data due to hundreds of spectral bands.Band selection is often applied firstly to reduce computational cost and facilitate subsequent tasks such as land-cover classification and higher level image analysis.In this paper,we propose a new band selection algorithm using sparse nonnegative matrix factorization(sparse NMF).Though acting as a clustering method for band selection,sparse NMF need not consider the distance metric between different spectral bands,which is often the key step for most common clustering-based band selection methods.By imposing sparsity on the coefficient matrix,the bands'clustering assignments can be easily indicated through the largest entry in each column of the matrix.Experimental results showed that sparse NMF provides considerable insight into the clustering-based band selection problem and the selected bands are good for land-cover classification. 展开更多
关键词 Hyperspectral Band selection Clustering Sparse nonnegative matrix factorization
AN EFFECTIVE INITIALIZATION FOR ORTHOGONAL NONNEGATIVE MATRIX FACTORIZATION 认领 引用 被引量：1
11
作者 Xuansheng Wang Xiaoyao Xie Linzhang Lu 《Journal of Computational Mathematics》 SCIE CSCD 2012年第1期34-46,共13页
The orthogonal nonnegative matrix factorization （ONMF） has many applications in a variety of areas such as data mining, information processing and pattern recognition. In this paper, we propose a novel initializatio... The orthogonal nonnegative matrix factorization （ONMF） has many applications in a variety of areas such as data mining, information processing and pattern recognition. In this paper, we propose a novel initialization method for the ONMF based on the Lanczos bidiagonalization and the nonnegative approximation of rank one matrix. Numerical experiments are given to show that our initialization strategy is effective and efficient. 展开更多
关键词 Lanczos bidiagonalization Orthogonal nonnegative matrix factorization Lowrank approximation Nonnegative approximation.
暂未订购 下载PDF
Learning a Subspace and Clustering Simultaneously with Manifold Regularized Nonnegative Matrix Factorization 认领 引用 被引量：1
12
作者 Feiping Nie Huimin Chen +2 位作者 Heng Huang Chris H.Q.Ding Xuelong Li 《Guidance, Navigation and Control》 2024年第3期147-165,共19页
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 probl... 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. 展开更多
关键词 Subspace learning clustering nonnegative matrix factorization manifold learning
Randomized Algorithms for Orthogonal Nonnegative Matrix Factorization 认领 引用 被引量：1
13
作者 Yong-Yong Chen Fang-Fang Xu 《Journal of the Operations Research Society of China》 EI CSCD 2023年第2期327-345,共19页
Orthogonal nonnegative matrix factorization(ONMF)is widely used in blind image separation problem,document classification,and human face recognition.The model of ONMF can be efficiently solved by the alternating direc... Orthogonal nonnegative matrix factorization(ONMF)is widely used in blind image separation problem,document classification,and human face recognition.The model of ONMF can be efficiently solved by the alternating direction method of multipliers and hierarchical alternating least squares method.When the given matrix is huge,the cost of computation and communication is too high.Therefore,ONMF becomes challenging in the large-scale setting.The random projection is an efficient method of dimensionality reduction.In this paper,we apply the random projection to ONMF and propose two randomized algorithms.Numerical experiments show that our proposed algorithms perform well on both simulated and real data. 展开更多
关键词 Orthogonal nonnegative matrix factorization Random projection method Dimensionality reduction Augmented lagrangian method Hierarchical alternating least squares algorithm
CloudNMF:A MapReduce Implementation of Nonnegative Matrix Factorization for Large-scale Biological Datasets 认领 引用 被引量：2
14
作者 Ruiqi Liao Yifan Zhang +1 位作者 Jihong Guan Shuigeng Zhou 《Genomics, Proteomics & Bioinformatics》 CAS CSCD 2014年第1期48-51,共4页
In the past decades,advances in high-throughput technologies have led to the generation of huge amounts of biological data that require analysis and interpretation.Recently,nonnegative matrix factorization （NMF） has... In the past decades,advances in high-throughput technologies have led to the generation of huge amounts of biological data that require analysis and interpretation.Recently,nonnegative matrix factorization （NMF） has been introduced as an efficient way to reduce the complexity of data as well as to interpret them,and has been applied to various fields of biological research.In this paper,we present CloudNMF,a distributed open-source implementation of NMF on a MapReduce framework.Experimental evaluation demonstrated that CloudNMF is scalable and can be used to deal with huge amounts of data,which may enable various kinds of a high-throughput biological data analysis in the cloud.CloudNMF is freely accessible at http：//admis.fudan.edu.cn/projects/CloudNMF.html. 展开更多
关键词 Nonnegative matrix factorization MapReduce Bioinformatics
NONNEGATIVE MATRIX FACTORIZATION WITH BAND CONSTRAINT 认领 引用
15
作者 Xiangxiang Zhu Jicheng Li Zhuosheng Zhang 《Journal of Computational Mathematics》 SCIE CSCD 2018年第6期761-775,共15页
In this paper, we study a band constrained nonnegative matrix factorization （band NMF） problem： for a given nonnegative matrix Y, decompose it as Y ≈ AX with A a nonnegative matrix and X a nonnegative block band m... In this paper, we study a band constrained nonnegative matrix factorization （band NMF） problem： for a given nonnegative matrix Y, decompose it as Y ≈ AX with A a nonnegative matrix and X a nonnegative block band matrix. This factorization model extends a single low rank subspace model to a mixture of several overlapping low rank subspaces, which not only can provide sparse representation, but also can capture signifi- cant grouping structure from a dataset. Based on overlapping subspace clustering and the capture of the level of overlap between neighbouring subspaces, two simple and practical algorithms are presented to solve the band NMF problem. Numerical experiments on both synthetic data and real images data show that band NMF enhances the performance of NMF in data representation and processing. 展开更多
关键词 Nonnegative matrix factorization Band structure Subspace clustering Sparserepresentation Image compression
暂未订购 下载PDF
CUR Based Initialization Strategy for Non-Negative Matrix Factorization in Application to Hyperspectral Unmixing 认领 引用
16
作者 Li Sun Gengxin Zhao Xinpeng Du 《Journal of Applied Mathematics and Physics》 2016年第4期614-617,共4页
Hyperspectral unmixing is a powerful tool for the remote sensing image mining. Nonnegative matrix factorization (NMF) has been adopted to deal with this issue, while the precision of unmixing is closely related with t... Hyperspectral unmixing is a powerful tool for the remote sensing image mining. Nonnegative matrix factorization (NMF) has been adopted to deal with this issue, while the precision of unmixing is closely related with the local minimizers of NMF. We present two novel initialization strategies that is based on CUR decomposition, which is physically meaningful. In the experimental test, NMF with the new initialization method is used to unmix the urban scene which was captured by airborne visible/infrared imaging spectrometer (AVIRIS) in 1997, numerical results show that the initialization methods work well. 展开更多
关键词 Nonnegative Matrix Factorization Hyperspectral Image Hyperspectral Unmixing Initialization Method
暂未订购 下载PDF
Optical properties of soil water-extractable organic matter respond to changes in irrigation water quality 认领 引用
17
作者 Mikhail BORISOVER Asher BAR-TAL +3 位作者 Nadezhda BUKHANOVSKY Anna BEREZKIN Marcos LADO Guy J.LEVY 《Pedosphere》 SCIE CAS CSCD 2026年第2期638-654,共17页
Treated wastewater(TWW)irrigation may harm soil and agricultural production.Adverse effects of TWW irrigation may be mitigated by changing irrigation water quality,e.g.,use of fresh water(FW),or management,e.g.,lower-... Treated wastewater(TWW)irrigation may harm soil and agricultural production.Adverse effects of TWW irrigation may be mitigated by changing irrigation water quality,e.g.,use of fresh water(FW),or management,e.g.,lower-frequency irrigation(LFI)with TWW.The influence of mitigation treatments on soil organic matter and water-extractable organic matter is unknown.To examine this influence,a study was conducted in an avocado orchard irrigated from 2009 with secondary TWW.Four parallel treatments were applied for six years(2016-2021),involving irrigation with FW,a 1:1 FW-TWW mixture,TWW at LFI(twice a week),and TWW irrigation three times a week as control.Soil samples from three depths were characterized for total organic and inorganic carbon and nitrogen and using Fourier-transform infrared spectra that were decomposed into components by nonnegative matrix factorization.Aqueous extracts of soil samples were characterized for dissolved organic carbon(DOC),inorganic carbon,and nitrogen concentrations,absorbance at254 nm(Abs254),and excitation-emission matrices of fluorescence that were decomposed into components by parallel factor analysis.The Abs254 and fluorescence of humic-like components in the soil extracts were the attributes most consistently responding to irrigation water quality.Irrigation with FW and a 1:1 FW-TWW mixture led to decreases in the concentrations of aromatic and humic-like constituents in the soil extracts compared with the control TWW irrigation.Changing soil irrigation management had no or little impact on those attributes compared with the control TWW irrigation.Results of the study support the use of optical properties related to the concentrations of ultraviolet-absorbing and light-emitting constituents in soil extracts,rather than the whole DOC concentration,as sensitive descriptors suitable for evaluating soil response in different agricultural scenarios. 展开更多
关键词 fluorescence Fourier-transform infrared spectroscopy humic-like substances nonnegative matrix factorization parallel factor analysis specific ultraviolet absorbance treated wastewater
暂未订购 下载PDF
An alternating direction algorithm for matrix completion with nonnegative factors 认领 引用 被引量：26
18
作者 Yangyang XU Wotao YIN +1 位作者 Zaiwen WEN Yin ZHANG 《Frontiers of Mathematics in China》 SCIE CSCD 2012年第2期365-384,共20页
This paper introduces an algorithm for the nonnegative matrix factorization-and-completion problem, which aims to find nonnegative low-rank matrices X and Y so that the product XY approximates a nonnegative data matri... This paper introduces an algorithm for the nonnegative matrix factorization-and-completion problem, which aims to find nonnegative low-rank matrices X and Y so that the product XY approximates a nonnegative data matrix M whose elements are partially known （to a certain accuracy）. This problem aggregates two existing problems： （i） nonnegative matrix factorization where all entries of M are given, and （ii） low-rank matrix completion where non- negativity is not required. By taking the advantages of both nonnegativity and low-rankness, one can generally obtain superior results than those of just using one of the two properties. We propose to solve the non-convex constrained least-squares problem using an algorithm based on tile classical alternating direction augmented Lagrangian method. Preliminary convergence properties of the algorithm and numerical simulation results are presented. Compared to a recent algorithm for nonnegative matrix factorization, the proposed algorithm produces factorizations of similar quality using only about half of the matrix entries. On tasks of recovering incomplete grayscale and hyperspeetral images, the proposed algorithm yields overall better qualities than those produced by two recent matrix-completion algorithms that do not exploit nonnegativity. 展开更多
关键词 nonnegative matrix factorization matrix completion alternating direction method hyperspectral unmixing
暂未订购 下载PDF
Semi-supervised multi-view clustering with dual hypergraph regularized partially shared non-negative matrix factorization 认领 引用 被引量：5
19
作者 ZHANG DongPing LUO YiHao +2 位作者 YU YuYuan ZHAO QiBin ZHOU GuoXu 《Science China(Technological Sciences)》 SCIE EI CAS CSCD 2022年第6期1349-1365,共17页
Real-world data can often be represented in multiple forms and views,and analyzing data from different perspectives allows for more comprehensive learning of the data,resulting in better data clustering results.Non-ne... Real-world data can often be represented in multiple forms and views,and analyzing data from different perspectives allows for more comprehensive learning of the data,resulting in better data clustering results.Non-negative matrix factorization(NMF)is used to solve the clustering problem to extract uniform discriminative low-dimensional features from multi-view data.Many clustering methods based on graph regularization have been proposed and proven to be effective,but ordinary graphs only consider pairwise relationships between samples.In order to learn the higher-order relationships that exist in the sample manifold and feature manifold of multi-view data,we propose a new semi-supervised multi-view clustering method called dual hypergraph regularized partially shared non-negative matrix factorization(DHPS-NMF).The complex manifold structure of samples and features is learned by constructing samples and feature hypergraphs.To improve the discrimination power of the obtained lowdimensional features,semi-supervised regression terms are incorporated into the model to effectively use the label information when capturing the complex manifold structure of the data.Ultimately,we conduct experiments on six real data sets and the results show that our algorithm achieves encouraging results in comparison with some methods. 展开更多
关键词 multi-view clustering semi-supervised learning nonnegative matrix factorization(NMF) dual hypergraph
暂未订购 下载PDF
Nonnegative tensor factorizations using an alternating direction method 认领 引用 被引量：5
20
作者 Xingju CAI Yannan CHEN Deren HAN 《Frontiers of Mathematics in China》 SCIE CSCD 2013年第1期3-18,共16页
The nonnegative tensor （matrix） factorization finds more and more applications in various disciplines including machine learning, data mining, and blind source separation, etc. In computation, the optimization probl... The nonnegative tensor （matrix） factorization finds more and more applications in various disciplines including machine learning, data mining, and blind source separation, etc. In computation, the optimization problem involved is solved by alternatively minimizing one factor while the others are fixed. To solve the subproblem efficiently, we first exploit a variable regularization term which makes the subproblem far from ill-condition. Second, an augmented Lagrangian alternating direction method is employed to solve this convex and well-conditioned regularized subproblem, and two accelerating skills are also implemented. Some preliminary numerical experiments are performed to show the improvements of the new method. 展开更多
关键词 Nonnegative matrix factorization nonnegative tensor factorization,nonnegative least squares alternating direction method
暂未订购 下载PDF
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
上一页 1 2 下一页 到第 页
在线咨询 使用帮助 返回顶部 意见反馈