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Vertex centrality of complex networks based on joint nonnegative matrix factorization and graph embedding

Chinese Physics B 2023年第1期32卷 634-645页

作者：卢鹏丽陈玮 School of Computer and Communication Lanzhou University of TechnologyLanzhou 730050China

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.

关键词： complex networks centrality joint nonnegative matrix factorization graph embedding smoothness strategy

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Feedback Influences Syllogistic Strategy: An Analysis based on joint nonnegative matrix factorization 18

Feedback Influences Syllogistic Strategy: An Analysis based ...

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18th International Conference on Cognitive Modelling, ICCM 2020 - Co-located with the 53rd Annual Meeting of the Society for Mathematical Psychology

作者： Riesterer, Nicolas Brand, Daniel Ragni, Marco Cognitive Computation Lab. University of Freiburg Georges-Koehler-Allee 52 Freiburg79110 Germany

ISBN: (纸本)9780998508245

Feedback for drawn inferences can lead to an adaption of future responses and underlying cognitive mechanisms. This article presents a reanalysis of recent hypothesis-driven experiments in syllogistic reasoning in which participants were presented with different feedback conditions (no feedback, 1s, 10s). We extend the original analysis, which only focused on no feedback vs. 1s feedback, by including the additional 10s condition. For our analysis, we rely on the data-driven theory- and hypothesis-agnostic joint nonnegative matrix factorization which allows us to contrast datasets based on the extraction of response patterns reflecting common and distinct response behavior. Our results support the previous claims that feedback does not generally boost logical reasoning ability but reduces the influence of biases against the response indicating that nothing logically follows from the premises. © 2020 Proceedings of ICCM 2020 - 18th International Conference on Cognitive Modelling. All Rights Reserved.

关键词： feedback joint nonnegative matrix factorization nvc bias syllogistic reasoning

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Hybrid clustering based on content and connection structure using joint nonnegative matrix factorization

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JOURNAL OF GLOBAL OPTIMIZATION 2019年第4期74卷 861-877页

作者： Du, Rundong Drake, Barry Park, Haesun Georgia Inst Technol Sch Math Atlanta GA 30332 USA Georgia Tech Res Inst Georgia Inst Technol Atlanta GA 30318 USA Georgia Inst Technol Sch Computat Sci & Engn Atlanta GA 30332 USA

A hybrid method called jointNMF is presented which is applied to latent information discovery from data sets that contain both text content and connection structure information. The new method jointly optimizes an integrated objective function, which is a combination of two components: the nonnegative matrix factorization (NMF) objective function for handling text content and the Symmetric NMF (SymNMF) objective function for handling network structure information. An effective algorithm for the joint NMF objective function is proposed so that the efficient method of block coordinate descent framework can be utilized. The proposed hybrid method simultaneously discovers content associations and related latent connections without any need for postprocessing of additional clustering. It is shown that the proposed method can also be applied when the text content is associated with hypergraph edges. An additional capability of the jointNMF is prediction of unknown network information which is illustrated using several real world problems such as citation recommendations of papers and leader detection in organizations. The proposed method can also be applied to general data expressed with both feature space vectors and pairwise similarities and can be extended to the case with multiple feature spaces or multiple similarity measures. Our experimental results illustrate multiple advantages of the proposed hybrid method when both content and connection structure information is available in the data for obtaining higher quality clustering results and discovery of new information such as unknown link prediction.

关键词： joint nonnegative matrix factorization Symmetric NMF Constrained low rank approximation Content clustering Graph clustering Hybrid content and connection structure analysis

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joint nonnegative matrix factorization for Underdetermined Blind Source Separation in Nonlinear Mixtures 1

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14th International Conference on Latent Variable Analysis and Signal Separation (LVA/ICA)

作者： Kopriva, Ivica Rudjer Boskovic Inst Div Elect Bijenicka Cesta 54 Zagreb 10000 Croatia

ISBN: (数字)9783319937649

ISBN: (纸本)9783319937649;9783319937632

An approach is proposed for underdetermined blind separation of nonnegative dependent (overlapped) sources from their nonlinear mixtures. The method performs empirical kernel maps based mappings of original data matrix onto reproducible kernel Hilbert spaces (RKHSs). Provided that sources comply with probabilistic model that is sparse in support and amplitude nonlinear underdetermined mixture model in the input space becomes overdetermined linear mixture model in RKHS comprised of original sources and their mostly second-order monomials. It is assumed that linear mixture models in different RKHSs share the same representation, i.e. the matrix of sources. Thus, we propose novel sparseness regularized joint nonnegative matrix factorization method to separate sources shared across different RKHSs. The method is validated comparatively on numerical problem related to extraction of eight overlapped sources from three nonlinear mixtures.

关键词： Underdetermined blind source separation Nonlinear mixtures Empirical kernel map joint nonnegative matrix factorization Sparseness

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INTEGRATION OF MULTIPLE GENOMIC IMAGING DATA FOR THE STUDY OF SCHIZOPHRENIA USING joint nonnegative matrix factorization

INTEGRATION OF MULTIPLE GENOMIC IMAGING DATA FOR THE STUDY O...

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Wang, Min Huang, Ting-Zhu Calhoun, Vince D. Fang, Jian Wang, Yu-Ping Univ Elect Sci & Technol China Sch Math Sci Res Ctr Image & Vis Comp Chengdu 611731 Sichuan Peoples R China Tulane Univ Dept Biomed Engn New Orleans LA 70118 USA Univ New Mexico Mind Res Network Albuquerque NM 87131 USA

ISBN: (纸本)9781509041176

Schizophrenia (SZ) is a complex disease caused by a lot genetic variants, epigenetic and brain region abnormalities. In this study, we adopted a joint nonnegative matrix factorization method to integrate three datasets including single nucleotide polymorphism (SNP), brain activity measured by functional magnetic resonance imaging (fMRI) and DNA Methylation to identify multi-dimensional modules associated with SZ. They are then used to study the coordination between regulatory mechanisms at multiple levels. This method projects multiple types of data onto a common feature space, in which heterogeneous variables with large coefficients on the same projected bases form a multi-dimensional module. The genomic factors in such modules have significant correlations and likely functional associations with brain activities. We applied this method to the real data analysis and identified multi-dimensional modules including SNP, fMRI and DNA methylation sites. These selected biomarkers were finally used to identify genes and voxels, which were confirmed to be significantly associated with SZ.

关键词： joint nonnegative matrix factorization SNP fMRI Methylation Feature selection

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Integration of multiple genomic imaging data for the study of schizophrenia using joint nonnegative matrix factorization

Integration of multiple genomic imaging data for the study o...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Min Wang Ting-Zhu Huang Vince D. Calhoun Jian Fang Yu-Ping Wang School of Mathematical Sciences/Research Center for Image and Vision Computing University of Electronic Science and Technology of China Chengdu Sichuan 611731 China The Mind Research Network University of New Mexico 87131 USA Department of Biomedical Engineering Tulane University New Orleans LA 70118 USA

ISBN: (纸本)9781509041183

关键词： joint nonnegative matrix factorization SNP fMRI Methylation Feature selection fMRI Single nucleotide polymorphism Feature extraction matrix decomposition Methylation sequence number protection Schizophrenia DNA Integration integration nonnegative DNA Methylation brain activity Eigenvector

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Exemplar-based voice conversion using joint nonnegative matrix factorization

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MULTIMEDIA TOOLS AND APPLICATIONS 2015年第22期74卷 9943-9958页

作者： Wu, Zhizheng Chng, Eng Siong Li, Haizhou Nanyang Technol Univ Sch Comp Engn Singapore 639798 Singapore Univ Edinburgh Ctr Speech Technol Res Edinburgh EH8 9YL Midlothian Scotland Nanyang Technol Univ Human Language Technol Dept Inst Infocomm Res Sch Comp Engn Singapore 639798 Singapore

Exemplar-based sparse representation is a nonparametric framework for voice conversion. In this framework, a target spectrum is generated as a weighted linear combination of a set of basis spectra, namely exemplars, extracted from the training data. This framework adopts coupled source-target dictionaries consisting of acoustically aligned source-target exemplars, and assumes they can share the same activation matrix. At runtime, a source spectrogram is factorized as a product of the source dictionary and the common activation matrix, which is applied to the target dictionary to generate the target spectrogram. In practice, either low-resolution mel-scale filter bank energies or high-resolution spectra are adopted in the source dictionary. Low-resolution features are flexible in capturing the temporal information without increasing the computational cost and the memory occupation significantly, while high-resolution spectra contain significant spectral details. In this paper, we propose a joint nonnegative matrix factorization technique to find the common activation matrix using low- and high-resolution features at the same time. In this way, the common activation matrix is able to benefit from low- and high-resolution features directly. We conducted experiments on the VOICES database to evaluate the performance of the proposed method. Both objective and subjective evaluations confirmed the effectiveness of the proposed methods.

关键词： Speech synthesis Voice conversion Exemplar Sparse representation nonnegative matrix factorization joint nonnegative matrix factorization

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