Literatura académica sobre el tema "Graph wavelets"
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Artículos de revistas sobre el tema "Graph wavelets"
Wu, Jiasong, Fuzhi Wu, Qihan Yang, Yan Zhang, Xilin Liu, Youyong Kong, Lotfi Senhadji y Huazhong Shu. "Fractional Spectral Graph Wavelets and Their Applications". Mathematical Problems in Engineering 2020 (6 de noviembre de 2020): 1–18. http://dx.doi.org/10.1155/2020/2568179.
Texto completoHammond, David K., Pierre Vandergheynst y Rémi Gribonval. "Wavelets on graphs via spectral graph theory". Applied and Computational Harmonic Analysis 30, n.º 2 (marzo de 2011): 129–50. http://dx.doi.org/10.1016/j.acha.2010.04.005.
Texto completoBastos, Anson, Abhishek Nadgeri, Kuldeep Singh, Toyotaro Suzumura y Manish Singh. "Learnable Spectral Wavelets on Dynamic Graphs to Capture Global Interactions". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 6 (26 de junio de 2023): 6779–87. http://dx.doi.org/10.1609/aaai.v37i6.25831.
Texto completoPaul, Okuwobi Idowu y Yong Hua Lu. "Facial Prediction and Recognition Using Wavelets Transform Algorithm and Technique". Applied Mechanics and Materials 666 (octubre de 2014): 251–55. http://dx.doi.org/10.4028/www.scientific.net/amm.666.251.
Texto completoXu, Mingxing, Wenrui Dai, Chenglin Li, Junni Zou, Hongkai Xiong y Pascal Frossard. "Graph Neural Networks With Lifting-Based Adaptive Graph Wavelets". IEEE Transactions on Signal and Information Processing over Networks 8 (2022): 63–77. http://dx.doi.org/10.1109/tsipn.2022.3140477.
Texto completoTay, D. B. H. y Z. Lin. "Highly localised near orthogonal graph wavelets". Electronics Letters 52, n.º 11 (mayo de 2016): 966–68. http://dx.doi.org/10.1049/el.2016.0482.
Texto completoTremblay, Nicolas y Pierre Borgnat. "Graph Wavelets for Multiscale Community Mining". IEEE Transactions on Signal Processing 62, n.º 20 (octubre de 2014): 5227–39. http://dx.doi.org/10.1109/tsp.2014.2345355.
Texto completoMasoumi, Majid y A. Ben Hamza. "Shape classification using spectral graph wavelets". Applied Intelligence 47, n.º 4 (9 de junio de 2017): 1256–69. http://dx.doi.org/10.1007/s10489-017-0955-7.
Texto completoYang, Zhirui, Yulan Hu, Sheng Ouyang, Jingyu Liu, Shuqiang Wang, Xibo Ma, Wenhan Wang, Hanjing Su y Yong Liu. "WaveNet: Tackling Non-stationary Graph Signals via Graph Spectral Wavelets". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 8 (24 de marzo de 2024): 9287–95. http://dx.doi.org/10.1609/aaai.v38i8.28781.
Texto completoSun, Qingyun, Jianxin Li, Beining Yang, Xingcheng Fu, Hao Peng y Philip S. Yu. "Self-Organization Preserved Graph Structure Learning with Principle of Relevant Information". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 4 (26 de junio de 2023): 4643–51. http://dx.doi.org/10.1609/aaai.v37i4.25587.
Texto completoTesis sobre el tema "Graph wavelets"
Behjat, Hamid. "Statistical Parametric Mapping of fMRI data using Spectral Graph Wavelets". Thesis, Linköpings universitet, Medicinsk informatik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-81143.
Texto completoJúnior, Alcebíades Dal Col. "Visual analytics via graph signal processing". Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-22102018-112358/.
Texto completoA transformada wavelet clássica tem sido amplamente usada no processamento de imagens e sinais, onde um sinal é decomposto em uma combinação de sinais de base. Analisando a contribuição individual dos sinais de base, pode-se inferir propriedades do sinal original. Esta tese apresenta uma visão geral da extensão da teoria clássica de processamento de sinais para grafos. Especificamente, revisamos a transformada de Fourier em grafo e as transformadas wavelet em grafo ambas fundamentadas na teoria espectral de grafos, e exploramos suas propriedades através de exemplos ilustrativos. As principais características das transformadas wavelet espectrais em grafo são apresentadas usando dados sintéticos e reais. Além disso, introduzimos nesta tese um método inovador para análise visual de redes dinâmicas, que utiliza a teoria de wavelets em grafo. Redes dinâmicas aparecem naturalmente em uma infinidade de aplicações de diferentes domínios. Analisar e explorar redes dinâmicas a fim de entender e detectar padrões e fenômenos é desafiador, fomentando o desenvolvimento de novas metodologias, particularmente no campo de análise visual. Nosso método permite a análise automática de um sinal definido nos vértices de uma rede, tornando possível a detecção de propriedades da rede. Especificamente, usamos uma aproximação da transformada wavelet em grafo para obter um conjunto de coeficientes wavelet, que são então usados para identificar padrões de atividade em redes de grande porte, incluindo a sua recorrência temporal. Os coeficientes wavelet naturalmente codificam variações espaciais e temporais do sinal, criando uma representação eficiente e com significado expressivo. Esse método permite explorar a evolução estrutural da rede e seus padrões ao longo do tempo. A eficácia da nossa abordagem é demonstrada usando diferentes cenários e comparações envolvendo redes dinâmicas reais.
Valdivia, Paola Tatiana Llerena. "Graph signal processing for visual analysis and data exploration". Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-15102018-165426/.
Texto completoO processamento de sinais é usado em uma ampla variedade de aplicações, desde o processamento digital de imagens até a biomedicina. Recentemente, algumas ferramentas do processamento de sinais foram estendidas ao contexto de grafos, permitindo seu uso em domínios irregulares. Entre outros, a Transformada de Fourier e a Transformada Wavelet foram adaptadas nesse contexto. O Processamento de Sinais em Grafos (PSG) é um novo campo com muitos aplicativos potenciais na exploração de dados. Nesta dissertação mostramos como ferramentas de processamento de sinal gráfico podem ser usadas para análise visual. Especificamente, o método de filtragem de dados porposto, baseado na filtragem de grafos espectrais, levou a visualizações de alta qualidade que foram atestadas qualitativa e quantitativamente. Por outro lado, usamos a transformada de wavelet em grafos para permitir a análise visual de dados massivos variantes no tempo, revelando fenômenos e eventos interessantes. As aplicações propostas do PSG para analisar visualmente os dados são um primeiro passo para incorporar o uso desta teoria nos métodos de visualização da informação. Muitas possibilidades do PSG podem ser exploradas melhorando a compreensão de fenômenos estáticos e variantes no tempo que ainda não foram descobertos.
Sharpnack, James. "Graph Structured Normal Means Inference". Research Showcase @ CMU, 2013. http://repository.cmu.edu/dissertations/246.
Texto completoLeandro, Jorge de Jesus Gomes. "Análise de formas usando wavelets em grafos". Universidade de São Paulo, 2014. http://www.teses.usp.br/teses/disponiveis/45/45134/tde-02072014-150049/.
Texto completoThis document describes the PhD thesis entitled Shape Analysis by using Wavelets on Graphs. The addressed theme is related to Computer Vision, particularly to the Characterization, Description and Classication topics. Amongst the methods presented in an extensive literature on Shape Analysis 2D, it is perceived a smaller presence of graph-based methods with arbitrary and irregular topologies. The contributions of this thesis aim at fullling this gap. A methodology based on the following pipeline is proposed: (i) Shape sampling, (ii) Samples structuring in graphs, (iii) Function dened on vertices, (iv) Multiscale analysis of graphs through the Spectral Wavelet Transform, (v) Features extraction from the Wavelet Transforms and (vi) Classication. For the stages (i), (ii), (iii), (v) and (vi), there are numerous possible approaches. One great challenge is to nd a proper combination of approaches from the several available alternatives, which may be able to yield an eective pipeline for our purposes. In particular, for the stage (iii), given a graph representing a shape, the challenge is to identify a feature, which may be dened over the graph vertices. This feature should capture the underlying inuence from the combinatorial structure of the entire network over each vertex, in multiple scales. The Spectral Graph Wavelet Transform will reveal such an underpining inuence over each vertex. Yielded results from experiments on 2D benchmarks shapes widely known in literature, as well as results from astronomy applications to the analysis of unlabeled galaxies shapes from the Sloan Digital Sky Survey and labeled galaxies shapes by the Galaxy Zoo 2 Project are presented, demonstrating the achievements of the proposed technique, in comparison to classic approaches such as the 2D Fourier Transform and the 2D Continuous Wavelet Transform.
Chedemail, Elie. "Débruitage de signaux définis sur des graphes de grande taille avec application à la confidentialité différentielle". Electronic Thesis or Diss., Rennes, École Nationale de la Statistique et de l'Analyse de l'Information, 2023. http://www.theses.fr/2023NSAI0001.
Texto completoOver the last decade, signal processing on graphs has become a very active area of research. Specifically, the number of applications using frames built from graphs, such as wavelets on graphs, has increased significantly. We consider in particular signal denoising on graphs via a wavelet tight frame decomposition. This approach is based on the thresholding of the wavelet coefficients using Stein’s unbiased risk estimate (SURE). We extend this methodology to large graphs using Chebyshev polynomial approximation, which avoids the decomposition of the graph Laplacian matrix. The main limitation is the computation of weights in the SURE expression, which includes a covariance term due to the overcomplete nature of the wavelet frame. The computation and storage of the latter is therefore necessary and impractical for large graphs. To estimate such covariance, we develop and analyze a Monte Carlo estimator based on the fast transform of random signals. This new denoising methodology finds a natural application in differential privacy whose purpose is to protect sensitive data used by algorithms. An experimental evaluation of its performance is carried out on graphs of varying size, using real and simulated data
IRFAN, MUHAMMAD ABEER. "Joint geometry and color denoising for 3D point clouds". Doctoral thesis, Politecnico di Torino, 2021. http://hdl.handle.net/11583/2912976.
Texto completoTremblay, Nicolas. "Réseaux et signal : des outils de traitement du signal pour l'analyse des réseaux". Thesis, Lyon, École normale supérieure, 2014. http://www.theses.fr/2014ENSL0938/document.
Texto completoThis thesis describes new tools specifically designed for the analysis of networks such as social, transportation, neuronal, protein, communication networks... These networks, along with the rapid expansion of electronic, IT and mobile technologies are increasingly monitored and measured. Adapted tools of analysis are therefore very much in demand, which need to be universal, powerful, and precise enough to be able to extract useful information from very different possibly large networks. To this end, a large community of researchers from various disciplines have concentrated their efforts on the analysis of graphs, well define mathematical tools modeling the interconnected structure of networks. Among all the considered directions of research, graph signal processing brings a new and promising vision : a signal is no longer defined on a regular n-dimensional topology, but on a particular topology defined by the graph. To apply these new ideas on the practical problems of network analysis paves the way to an analysis firmly rooted in signal processing theory. It is precisely this frontier between signal processing and network science that we explore throughout this thesis, as shown by two of its major contributions. Firstly, a multiscale version of community detection in networks is proposed, based on the recent definition of graph wavelets. Then, a network-adapted bootstrap method is introduced, that enables statistical estimation based on carefully designed graph resampling schemes
Zheng, Xuebin. "Wavelet-based Graph Neural Networks". Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/27989.
Texto completoKotzagiannidis, Madeleine S. "From spline wavelet to sampling theory on circulant graphs and beyond : conceiving sparsity in graph signal processing". Thesis, Imperial College London, 2017. http://hdl.handle.net/10044/1/56614.
Texto completoLibros sobre el tema "Graph wavelets"
Tree structured function estimation with Haar wavelets. Hamburg: Verlag Dr. Kovač, 1999.
Buscar texto completoHarmonic Analysis. American Mathematical Society, 2018.
Buscar texto completoCapítulos de libros sobre el tema "Graph wavelets"
Farsi, Carla, Elizabeth Gillaspy, Sooran Kang y Judith Packer. "Wavelets and Graph C ∗-Algebras". En Excursions in Harmonic Analysis, Volume 5, 35–86. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54711-4_3.
Texto completoGong, Bo, Benjamin Schullcke, Sabine Krueger-Ziolek y Knut Moeller. "EIT Imaging Regularization Based on Spectral Graph Wavelets". En XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016, 1280–84. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-32703-7_245.
Texto completoMasoumi, Majid, Mahsa Rezaei y A. Ben Hamza. "Shape Analysis of Carpal Bones Using Spectral Graph Wavelets". En Signals and Communication Technology, 419–36. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03574-7_12.
Texto completoKuncheva, Zhana y Giovanni Montana. "Multi-scale Community Detection in Temporal Networks Using Spectral Graph Wavelets". En Personal Analytics and Privacy. An Individual and Collective Perspective, 139–54. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71970-2_12.
Texto completoRustamov, Raif M. y Leonidas J. Guibas. "Wavelets on Graphs via Deep Learning". En Signals and Communication Technology, 207–22. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03574-7_5.
Texto completoYadav, Rakesh Kumar, Abhishek, Prashant Shukla, Neelanjana Jaiswal, Brijesh Kumar Chaurasia y Shekhar Verma. "Graph Convolutional Neural Network Using Wavelet Transform". En Communications in Computer and Information Science, 223–36. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-8896-6_18.
Texto completoCioacă, Teodor, Bogdan Dumitrescu y Mihai-Sorin Stupariu. "Graph-Based Wavelet Multiresolution Modeling of Multivariate Terrain Data". En Signals and Communication Technology, 479–507. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03574-7_15.
Texto completoHammond, David K., Pierre Vandergheynst y Rémi Gribonval. "The Spectral Graph Wavelet Transform: Fundamental Theory and Fast Computation". En Signals and Communication Technology, 141–75. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03574-7_3.
Texto completoLi, Xin y Hui Li. "Research on Bearing Fault Feature Extraction Based on Graph Wavelet". En Intelligent Computing Theories and Application, 208–20. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13870-6_17.
Texto completoLürig, Christoph, Roberto Grosso y Thomas Ertl. "Combining Wavelet Transform and Graph Theory for Feature Extraction and Visualization". En Eurographics, 105–14. Vienna: Springer Vienna, 1997. http://dx.doi.org/10.1007/978-3-7091-6876-9_10.
Texto completoActas de conferencias sobre el tema "Graph wavelets"
Teimury, Fatemeh, Soumyasundar Pal, Arezou Amini y Mark Coates. "Estimation of time-series on graphs using Bayesian graph convolutional neural networks". En Wavelets and Sparsity XVIII, editado por Yue M. Lu, Manos Papadakis y Dimitri Van De Ville. SPIE, 2019. http://dx.doi.org/10.1117/12.2530046.
Texto completoLi, Haotian y Naoki Saito. "Metrics of graph Laplacian eigenvectors". En Wavelets and Sparsity XVIII, editado por Yue M. Lu, Manos Papadakis y Dimitri Van De Ville. SPIE, 2019. http://dx.doi.org/10.1117/12.2528644.
Texto completoPetrovic, Miljan y Dimitri Van De Ville. "Slepian guided filtering of graph signals". En Wavelets and Sparsity XVIII, editado por Yue M. Lu, Manos Papadakis y Dimitri Van De Ville. SPIE, 2019. http://dx.doi.org/10.1117/12.2528827.
Texto completoBalan, Radu V. y Naveed Haghani. "Discrete optimizations using graph convolutional networks". En Wavelets and Sparsity XVIII, editado por Yue M. Lu, Manos Papadakis y Dimitri Van De Ville. SPIE, 2019. http://dx.doi.org/10.1117/12.2529432.
Texto completoAchard, Sophie, Pierre Borgnat, Irène Gannaz y Marine Roux. "Wavelet-based graph inference using multiple testing". En Wavelets and Sparsity XVIII, editado por Yue M. Lu, Manos Papadakis y Dimitri Van De Ville. SPIE, 2019. http://dx.doi.org/10.1117/12.2529193.
Texto completoGirault, Benjamin, Shrikanth S. Narayanan y Antonio Ortega. "Local stationarity of graph signals: insights and experiments". En Wavelets and Sparsity XVII, editado por Yue M. Lu, Manos Papadakis y Dimitri Van De Ville. SPIE, 2017. http://dx.doi.org/10.1117/12.2274584.
Texto completoCloninger, Alexander. "Prediction models for graph-linked data with localized regression". En Wavelets and Sparsity XVII, editado por Yue M. Lu, Manos Papadakis y Dimitri Van De Ville. SPIE, 2017. http://dx.doi.org/10.1117/12.2271840.
Texto completoSilva, Arlei, Xuan Hong Dang, Prithwish Basu, Ambuj Singh y Ananthram Swami. "Graph Wavelets via Sparse Cuts". En KDD '16: The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2939672.2939777.
Texto completoRamchandran, Kannan. "Speeding up sparse signal recovery using sparse-graph codes (Conference Presentation)". En Wavelets and Sparsity XVII, editado por Yue M. Lu, Manos Papadakis y Dimitri Van De Ville. SPIE, 2017. http://dx.doi.org/10.1117/12.2281055.
Texto completoKotzagiannidis, Madeleine S. y Pier Luigi Dragotti. "Higher-order graph wavelets and sparsity on circulant graphs". En SPIE Optical Engineering + Applications, editado por Manos Papadakis, Vivek K. Goyal y Dimitri Van De Ville. SPIE, 2015. http://dx.doi.org/10.1117/12.2192003.
Texto completoInformes sobre el tema "Graph wavelets"
Crovella, Mark y Eric Kolaczyk. Graph Wavelets for Spatial Traffic Analysis. Fort Belvoir, VA: Defense Technical Information Center, julio de 2002. http://dx.doi.org/10.21236/ada442573.
Texto completoAWARE INC CAMBRIDGE MA. The Performance of Wavelets for Data Compression in Selected Military Applications. Volume 2. Supplementary Tables and Graphs. Fort Belvoir, VA: Defense Technical Information Center, enero de 1990. http://dx.doi.org/10.21236/ada219231.
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