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nonnegative matrix factorization for link prediction in directed complex networks using PageRank and asymmetric link clustering information

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EXPERT SYSTEMS WITH APPLICATIONS 2020年 148卷 113290-113290页

作者： Chen, Guangfu Xu, Chen Wang, Jingyi Feng, Jianwen Feng, Jiqiang Shenzhen Univ Coll Elect & Informat Engn Shenzhen Peoples R China Shenzhen Univ Coll Math & Stat Shenzhen Peoples R China

The aim of link prediction is to predict missing links in current networks or new links in future networks. Almost all the existing directed link prediction algorithms only take into account the links direction formation but ignored the abundant network topological information such as local and global structures. Therefore, how to preserve both local and global structure information is an important issue for directed link prediction. To solve this problem, in this paper, we are motivated to propose a novel nonnegative matrix factorization via Asymmetric link clustering and PageRank model, namely NMF-AP. Specifically, we utilize the PageRank algorithm to calculate the influence score of the node, which captures the global network structure information. While we employ the asymmetric link clustering method to calculate the link clustering coefficient score, which preserves the local network structure information. By jointly optimizing them in the nonnegative matrix factorization model, our model can preserve both the local and global information at the same time. Besides, we provide an effective the multiplicative updating rules to learn the parameter of NMF-AP. Extensive experiments are conducted on ten real-world directed networks, experiment results demonstrate that the method NMF-AP outperforms state-of-the-art link prediction methods. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Link prediction nonnegative matrix factorization PageRank Asymmetric link clustering

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nonnegative matrix factorization Requires Irrationality

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SIAM JOURNAL ON APPLIED ALGEBRA AND GEOMETRY 2017年第1期1卷 285-307页

作者： Chistikov, Dmitry Kiefer, Stefan Marusic, Ines Shirmohammadi, Mahsa Worrell, James Max Planck Inst Software Syst MPI SWS D-67663 Kaiserslautern Germany Max Planck Inst Software Syst MPI SWS D-66123 Saarbrucken Germany Univ Warwick Coventry CV4 7AL W Midlands England Univ Oxford Oxford OX1 3QD England

nonnegative matrix factorization (NMF) is the problem of decomposing a given nonnegative is n x m so matrix M into a product of a nonnegative is n x d matrix W and a nonnegative d x m to matrix H. A longstanding open question, posed by Cohen and Rothblum in 1993, is whether a rational matrix M always has an NMF of minimal inner dimension d whose factors W and H are also rational. We answer this question negatively, by exhibiting a matrix for which W and H require irrational entries.

关键词： nonnegative matrix factorization nonnegative rank

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nonnegative matrix factorization with manifold regularization and maximum discriminant information

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2015年第5期6卷 837-846页

作者： Hu, Wenjun Choi, Kup-Sze Tao, Jianwen Jiang, Yunliang Wang, Shitong Huzhou Univ Sch Informat Engn Huzhou Zhejiang Peoples R China Jiangnan Univ Sch Digital Media Wuxi Jiangsu Peoples R China Hong Kong Polytech Univ Ctr Smart Hlth Sch Nursing Hong Kong Hong Kong Peoples R China Zhejiang Univ Sch Informat Sci & Engn Ningbo Inst Technol Ningbo Zhejiang Peoples R China

nonnegative matrix factorization (NMF) has been successfully used in different applications including computer vision, pattern recognition and text mining. NMF aims to decompose a data matrix into the product of two matrices (respectively denoted as the basis vectors and the encoding vectors), whose entries are constrained to be nonnegative. Unlike the ordinary NMF, we propose a novel NMF, denoted as MMNMF, which considers both geometrical information and discriminative information hidden in the data. The geometrical information is discovered by minimizing the distance among the encoding vectors, while the discriminative information is uncovered by maximizing the distance among base vectors. Clustering experiments are performed on the real-world data sets of faces, images, and documents to demonstrate the effectiveness of the proposed algorithm.

关键词： nonnegative matrix factorization Manifold regularization Maximum information Clustering

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nonnegative matrix factorization for spectral data analysis

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LINEAR ALGEBRA AND ITS APPLICATIONS 2006年第1期416卷 29-47页

作者： Pauca, V. Paul Piper, J. Plemmons, Robert J. Wake Forest Univ Dept Comp Sci Winston Salem NC 27109 USA Wake Forest Univ Dept Math Winston Salem NC 27109 USA

Data analysis is pervasive throughout business, engineering and science. Very often the data to be analyzed is nonnegative, and it is often preferable to take this constraint into account in the analysis process. Here we are concerned with the application of analyzing data obtained using astronomical spectrometers, which provide spectral data, which is inherently nonnegative. The identification and classification of space objects that cannot be imaged in the normal way with telescopes is an important but difficult problem for tracking thousands of objects, including satellites, rocket bodies, debris, and asteroids, in orbit around the earth. In this paper, we develop an effective nonnegative matrix factorization algorithm with novel smoothness constraints for unmixing spectral reflectance data for space object identification and classification purposes. Promising numerical results are presented using laboratory and simulated datasets. (c) 2005 Elsevier Inc. All rights reserved.

关键词： nonnegative matrix factorization spectral data blind source separation data mining space object identification and classification

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nonnegative matrix factorization Based on Node Centrality for Community Detection

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ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA 2023年第6期17卷 1-21页

作者： Su, Sixing Guan, Jiewen Chen, Bilian Huang, Xin Xiamen Univ Dept Automat Xiamen 361005 Peoples R China Xiamen Key Lab Big Data Intelligent Anal & Decis Xiamen 361005 Peoples R China Hong Kong Baptist Univ Dept Comp Sci Hong Kong Peoples R China

Community detection is an important topic in network analysis, and recently many community detection methods have been developed on top of the nonnegative matrix factorization (NMF) technique. Most NMFbased community detection methods only utilize the first-order proximity information in the adjacency matrix, which has some limitations. Besides, many NMF-based community detection methods involve sparse regularizations to promote clearer community memberships. However, in most of these regularizations, different nodes are treated equally, which seems unreasonable. To dismiss the above limitations, this article proposes a community detection method based on node centrality under the framework of NMF. Specifically, we design a new similarity measure which considers the proximity of higher-order neighbors to form a more informative graph regularization mechanism, so as to better refine the detected communities. Besides, we introduce the node centrality and Gini impurity to measure the importance of nodes and sparseness of the community memberships, respectively. Then, we propose a novel sparse regularization mechanism which forces nodes with higher node centrality to have smaller Gini impurity. Extensive experimental results on a variety of real-world networks show the superior performance of the proposed method over thirteen stateof-the-art methods.

关键词： Community detection nonnegative matrix factorization similarity measure sparse regularization optimization

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nonnegative matrix factorization-based bioinformatics analysis reveals that TPX2 and SELENBP1 are two predictors of the inner sub-consensuses of lung adenocarcinoma

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CANCER MEDICINE 2021年第24期10卷 9058-9077页

作者： Wang, Haiwei Wang, Xinrui Xu, Liangpu Cao, Hua Zhang, Ji Fujian Med Univ Fujian Matern & Child Hlth Hosp Fujian Key Lab Prenatal Diag & Birth Defect Affiliated Hosp Fuzhou Fujian Peoples R China Natl Hlth & Family Planning Commiss Key Lab Tech Evaluat Fertil Regulat Nonhuman Prim Fuzhou Fujian Peoples R China Shanghai Jiao Tong Univ Rui Jin Hosp Shanghai Inst Hematol State Key Lab Med GenomSch Med Shanghai Peoples R China

Background Lung adenocarcinoma (LUAD) is a heterogeneous disease. However the inner sub-groups of LUAD have not been fully studied. Markers predicted the sub-groups and prognosis of LUAD are badly needed. Aims To identify biomarkers associated with the sub-groups and prognosis of LUAD. Materials and Methods Using nonnegative matrix factorization (NMF) clustering, LUAD patients from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) datasets and LUAD cell lines from Genomics of Drug Sensitivity in Cancer (GDSC) dataset were divided into different sub-consensuses based on the gene expression profiling. The overall survival of LUAD patients in each sub-consensus was determined by Kaplan-Meier survival analysis. The common genes which were differentially expressed in each sub-consensus of LUAD patients and LUAD cell lines were identified using TBtools. The predictive accuracy of TPX2 and SELENBP1 for theinner sub-consensuses of LUAD was determined by Receiver operator characteristic (ROC) analysis. The Kaplan-Meier survival analysis was also used to test the prognostic significance of TPX2 and SELENBP1 in LUAD patients. Results Using nonnegative matrix factorization clustering, LUAD patients in The Cancer Genome Atlas (TCGA), , , , , and datasets were divided into three sub-consensuses. Sub-consensus3 LUAD patients were with low overall survival and were with high TP53 mutations. Similarly, LUAD cell lines were also divided into three sub-consensuses by NMF method, and sub-consensus2 cell lines were resistant to EGFR inhibitors. Identification of the common genes which were differentially expressed in different sub-consensuses of LUAD patients and LUAD cell lines revealed that TPX2 was highly expressed in sub-consensus3 LUAD patients and sub-consensus2 LUAD cell lines. On the contrary, SELENBP1 was highly expressed in sub-consensus1 LUAD patients and sub-consensus1 LUAD cell lines. The expression levels of TPX2 and SELENBP1 could distinguish sub-consensus3 LU

关键词： nonnegative matrix factorization SELENBP1 sub-consensus of lung adenocarcinoma TPX2

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nonnegative matrix factorization with Fixed L2-Norm Constraint

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2019年第7期38卷 3211-3226页

作者： Yang, Zuyuan Hu, Yifei Liang, Naiyao Lv, Jun Guangdong Univ Technol Sch Automat Guangdong Key Lab IoT Informat Technol Guangzhou 510006 Guangdong Peoples R China

nonnegative matrix factorization (NMF) is a very attractive scheme in learning data representation, and constrained NMF further improves its ability. In this paper, we focus on the L2-norm constraint due to its wide applications in face recognition, hyperspectral unmixing, and so on. A new algorithm of NMF with fixed L2-norm constraint is proposed by using the Lagrange multiplier scheme. In our method, we derive the involved Lagrange multiplier and learning rate which are hard to tune. As a result, our method can preserve the constraint exactly during the iteration. Simulations in both computer-generated data and real-world data show the performance of our algorithm.

关键词： nonnegative matrix factorization Multiplicative updates L2-norm Constrained NMF

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nonnegative matrix factorization for clustering ensemble based on dark knowledge

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KNOWLEDGE-BASED SYSTEMS 2019年第Jan.1期163卷 624-631页

作者： Ye, Wenting Wang, Hongjun Yan, Shan Li, Tianrui Yang, Yan Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu 60031 Sichuan Peoples R China

Traditional cluster ensemble (CE) methods use labels produced by base learning algorithms to obtain an ensemble result. These base learning algorithms can also obtain other information, such as parameter, covariance, or probability data, which is called dark knowledge. In this paper, we propose a method for integrating dark knowledge, which is usually ignored, into the ensemble learning process. This provides more information about the base clustering. We apply nonnegative matrix factorization (NMF) to the clustering ensemble model based on dark knowledge. First, different base clustering results are obtained by using various clustering configurations, before dark knowledge of every base clustering algorithm is extracted. NMF is then applied to the dark knowledge to obtain integrated results. Experimental results show that the method outperforms other clustering ensemble techniques. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Cluster ensemble nonnegative matrix factorization Dark knowledge

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nonnegative matrix factorization with quadratic programming

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NEUROCOMPUTING 2008年第10-12期71卷 2309-2320页

作者： Zdunek, Rafal Cichocki, Andrzej RIKEN Brain Sci Inst Lab Adv Brain Signal Proc Wako Saitama 3510198 Japan Wroclaw Univ Technol Inst Telecommun Teleinformat & Acoust PL-50370 Wroclaw Poland Polish Acad Sci IBS PAN PL-00901 Warsaw Poland

nonnegative matrix factorization (NMF) solves the following problem: find such nonnegative matrices A is an element of R-+(IxJ) and X is an element of R-+(JxK) that Y congruent to AX, given only Y is an element of R-IxK and the assigned index J (K >> 1 >= J). Basically, the factorization is achieved by alternating minimization of a given cost function subject to nonnegativity constraints. In the paper, we propose to use quadratic programming (QP) to solve the minimization problems. The Tikhonov regularized squared Euclidean cost function is extended with a logarithmic barrier function (which satisfies nonnegativity constraints), and then using second-order Taylor expansion, a QP problem is formulated. This problem is solved with some trust-region subproblem algorithm. The numerical tests are performed on the blind source separation problems. (C) 2008 Elsevier B.V. All rights reserved.

关键词： nonnegative matrix factorization blind source separation quadratic programming

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nonnegative matrix factorization Based Heterogeneous Graph Embedding Method for Trigger-Action Programming in IoT

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2022年第2期18卷 1231-1239页

作者： Xing, Yongheng Hu, Liang Zhang, Xiaolu Wu, Gang Wang, Feng Jilin Univ Coll Comp Sci & Technol Engn Res Ctr Network Technol & Applcat Software Minist Educ Changchun 130012 Peoples R China Univ Texas San Antonio Dept Informat Syst & Cyber Secur San Antonio TX 78249 USA

Nowadays, users can personalize Internet of Things (IoT) devices/web services via trigger-action programming (TAP). As the number of connected entities grows, the relations of triggers and actions become progressively complex (i.e., the heterogeneity of TAP), which becomes a challenge for existing models to completely preserve the heterogeneous data and semantic information in trigger and action. To address this issue, in this article, we propose IoT nonnegative matrix factorization (IoT-NMF), a NMF-based heterogeneous graph embedding method for TAP. Prior to using IoT-NMF, we map triggers and actions to an IoT heterogeneous information network, from which we can extract three structures that preserve heterogeneous relations in triggers and actions. IoT-NMF can factorize the structures simultaneously for getting low-dimensional representation vectors of the triggers and actions, which can be further utilized in Artificial Intelligence of Things applications (e.g., TAP rule recommendation). Finally, we demonstrate the proposed approach using an if this then that (IFTTT) dataset. The result shows that IoT-NMF outperforms the state-of-the-art approaches.

关键词： Internet of Things Semantics Programming Feature extraction Cameras Data mining Machine learning Graph embedding heterogeneous information networks Internet of Things (IoT) nonnegative matrix factorization trigger-action programming (TAP)

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