Artículos de revistas sobre el tema "Multi-dimensional graph signal processing"
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Zheng, Xianwei, Yuan Yan Tang, Jiantao Zhou, Jianjia Pan, Shouzhi Yang, Youfa Li y Patrick S. P. Wang. "Multi-Level Downsampling of Graph Signals via Improved Maximum Spanning Trees". International Journal of Pattern Recognition and Artificial Intelligence 33, n.º 03 (19 de febrero de 2019): 1958005. http://dx.doi.org/10.1142/s0218001419580059.
Texto completoLiao, Kefei, Zerui Yu, Ningbo Xie y Junzheng Jiang. "Joint Estimation of Azimuth and Distance for Far-Field Multi Targets Based on Graph Signal Processing". Remote Sensing 14, n.º 5 (24 de febrero de 2022): 1110. http://dx.doi.org/10.3390/rs14051110.
Texto completoYankelevsky, Yael y Michael Elad. "Finding GEMS: Multi-Scale Dictionaries For High-Dimensional Graph Signals". IEEE Transactions on Signal Processing 67, n.º 7 (1 de abril de 2019): 1889–901. http://dx.doi.org/10.1109/tsp.2019.2899822.
Texto completoJian, Xingchao, Feng Ji y Wee Peng Tay. "Generalizing Graph Signal Processing: High Dimensional Spaces, Models and Structures". Foundations and Trends® in Signal Processing 17, n.º 3 (2023): 209–90. http://dx.doi.org/10.1561/2000000119.
Texto completoXiong, Chao, Wen Li, Yun Liu y Minghui Wang. "Multi-Dimensional Edge Features Graph Neural Network on Few-Shot Image Classification". IEEE Signal Processing Letters 28 (2021): 573–77. http://dx.doi.org/10.1109/lsp.2021.3061978.
Texto completoMathur, Priyanka y Vijay Kumar Chakka. "Graph Signal Processing Based Cross-Subject Mental Task Classification Using Multi-Channel EEG Signals". IEEE Sensors Journal 22, n.º 8 (15 de abril de 2022): 7971–78. http://dx.doi.org/10.1109/jsen.2022.3156152.
Texto completoPark, Han-Mu y Kuk-Jin Yoon. "Exploiting multi-layer graph factorization for multi-attributed graph matching". Pattern Recognition Letters 127 (noviembre de 2019): 85–93. http://dx.doi.org/10.1016/j.patrec.2018.09.024.
Texto completoRakhimberdina, Zarina, Xin Liu y Tsuyoshi Murata. "Population Graph-Based Multi-Model Ensemble Method for Diagnosing Autism Spectrum Disorder". Sensors 20, n.º 21 (22 de octubre de 2020): 6001. http://dx.doi.org/10.3390/s20216001.
Texto completoLi, Shuang, Bing Liu y Chen Zhang. "Regularized Embedded Multiple Kernel Dimensionality Reduction for Mine Signal Processing". Computational Intelligence and Neuroscience 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/4920670.
Texto completoOselio, Brandon, Alex Kulesza y Alfred O. Hero. "Multi-Layer Graph Analysis for Dynamic Social Networks". IEEE Journal of Selected Topics in Signal Processing 8, n.º 4 (agosto de 2014): 514–23. http://dx.doi.org/10.1109/jstsp.2014.2328312.
Texto completoLézoray, Olivier. "Hierarchical morphological graph signal multi-layer decomposition for editing applications". IET Image Processing 14, n.º 8 (19 de junio de 2020): 1549–60. http://dx.doi.org/10.1049/iet-ipr.2019.0576.
Texto completoLi, Yuzhong, Wenming Tang y Guixiong Liu. "HPEFT for Hierarchical Heterogeneous Multi-DAG in a Multigroup Scan UPA System". Electronics 8, n.º 5 (5 de mayo de 2019): 498. http://dx.doi.org/10.3390/electronics8050498.
Texto completoZhang, Guoxing, Haixiao Wang y Yuanpu Yin. "Multi-type Parameter Prediction of Traffic Flow Based on Time-space Attention Graph Convolutional Network". International Journal of Circuits, Systems and Signal Processing 15 (11 de agosto de 2021): 902–12. http://dx.doi.org/10.46300/9106.2021.15.97.
Texto completoMehta, Sumet, Bi-Sheng Zhan y Xiang-Jun Shen. "Weighted Neighborhood Preserving Ensemble Embedding". Electronics 8, n.º 2 (16 de febrero de 2019): 219. http://dx.doi.org/10.3390/electronics8020219.
Texto completoSlota, George M., Cameron Root, Karen Devine, Kamesh Madduri y Sivasankaran Rajamanickam. "Scalable, Multi-Constraint, Complex-Objective Graph Partitioning". IEEE Transactions on Parallel and Distributed Systems 31, n.º 12 (1 de diciembre de 2020): 2789–801. http://dx.doi.org/10.1109/tpds.2020.3002150.
Texto completoHuang, Yanquan, Haoliang Yuan y Loi Lei Lai. "Latent multi-view semi-supervised classification by using graph learning". International Journal of Wavelets, Multiresolution and Information Processing 18, n.º 05 (20 de junio de 2020): 2050039. http://dx.doi.org/10.1142/s0219691320500393.
Texto completoLiu, Zhi, Jixin Bian, Deju Zhang, Yang Chen, Guojiang Shen y Xiangjie Kong. "Dynamic Multi-View Coupled Graph Convolution Network for Urban Travel Demand Forecasting". Electronics 11, n.º 16 (21 de agosto de 2022): 2620. http://dx.doi.org/10.3390/electronics11162620.
Texto completoTugnait, Jitendra K. "Sparse-Group Lasso for Graph Learning From Multi-Attribute Data". IEEE Transactions on Signal Processing 69 (2021): 1771–86. http://dx.doi.org/10.1109/tsp.2021.3057699.
Texto completoTugnait, Jitendra K. "Deviance Tests for Graph Estimation From Multi-Attribute Gaussian Data". IEEE Transactions on Signal Processing 68 (2020): 5632–47. http://dx.doi.org/10.1109/tsp.2020.3023575.
Texto completoIoannidis, Vassilis N., Antonio G. Marques y Georgios B. Giannakis. "Tensor Graph Convolutional Networks for Multi-Relational and Robust Learning". IEEE Transactions on Signal Processing 68 (2020): 6535–46. http://dx.doi.org/10.1109/tsp.2020.3028495.
Texto completoRahimi, Sahere, Ali Aghagolzadeh y Mehdi Ezoji. "Human action recognition based on the Grassmann multi-graph embedding". Signal, Image and Video Processing 13, n.º 2 (6 de septiembre de 2018): 271–79. http://dx.doi.org/10.1007/s11760-018-1354-1.
Texto completoZhang, Dongxiao, Pierre-Marc Jodoin, Cuihua Li, Yundong Wu y Guorong Cai. "Novel Graph Cuts Method for Multi-Frame Super-Resolution". IEEE Signal Processing Letters 22, n.º 12 (diciembre de 2015): 2279–83. http://dx.doi.org/10.1109/lsp.2015.2477079.
Texto completoEdwards, Michael, Xianghua Xie, Robert I. Palmer, Gary K. L. Tam, Rob Alcock y Carl Roobottom. "Graph convolutional neural network for multi-scale feature learning". Computer Vision and Image Understanding 194 (mayo de 2020): 102881. http://dx.doi.org/10.1016/j.cviu.2019.102881.
Texto completoZhang, Jingwei, Zhongdao Wang, Yali Li y Shengjin Wang. "Node-Adaptive Multi-Graph Fusion Using Extreme Value Theory". IEEE Signal Processing Letters 27 (2020): 351–55. http://dx.doi.org/10.1109/lsp.2020.2970811.
Texto completoLi, Guodong, Xvan Qin, He Liu, Kaiyuan Jiang y Aili Wang. "Modulation Recognition of Digital Signal Using Graph Feature and Improved K-Means". Electronics 11, n.º 20 (13 de octubre de 2022): 3298. http://dx.doi.org/10.3390/electronics11203298.
Texto completoLi, Han, Xinyu Wang, Zhongguo Yang, Sikandar Ali, Ning Tong y Samad Baseer. "Correlation-Based Anomaly Detection Method for Multi-sensor System". Computational Intelligence and Neuroscience 2022 (31 de mayo de 2022): 1–13. http://dx.doi.org/10.1155/2022/4756480.
Texto completoAkbarian, Behnaz y Abbas Erfanian. "A framework for seizure detection using effective connectivity, graph theory, and multi-level modular network". Biomedical Signal Processing and Control 59 (mayo de 2020): 101878. http://dx.doi.org/10.1016/j.bspc.2020.101878.
Texto completoXia, Wei, Junbin Chen y Lisha Yu. "Distributed Adaptive Multi-Task Learning Based on Partially Observed Graph Signals". IEEE Transactions on Signal and Information Processing over Networks 7 (2021): 522–38. http://dx.doi.org/10.1109/tsipn.2021.3101109.
Texto completoLi, Juan-Hui, Chang-Dong Wang, Pei-Zhen Li y Jian-Huang Lai. "Discriminative metric learning for multi-view graph partitioning". Pattern Recognition 75 (marzo de 2018): 199–213. http://dx.doi.org/10.1016/j.patcog.2017.06.012.
Texto completoWu, Jiaxin, Sheng-hua Zhong y Yan Liu. "Dynamic graph convolutional network for multi-video summarization". Pattern Recognition 107 (noviembre de 2020): 107382. http://dx.doi.org/10.1016/j.patcog.2020.107382.
Texto completoGutiérrez-Gómez, Leonardo, Alexandre Bovet y Jean-Charles Delvenne. "Multi-Scale Anomaly Detection on Attributed Networks". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 01 (3 de abril de 2020): 678–85. http://dx.doi.org/10.1609/aaai.v34i01.5409.
Texto completoKhachan, Mohammed, Patrick Chenin y Hafsa Deddi. "Polyhedral Representation and Adjacency Graph in n-dimensional Digital Images". Computer Vision and Image Understanding 79, n.º 3 (septiembre de 2000): 428–41. http://dx.doi.org/10.1006/cviu.2000.0859.
Texto completoTugnait, Jitendra. "Corrections to “Sparse-Group Lasso for Graph Learning From Multi-Attribute Data”". IEEE Transactions on Signal Processing 69 (2021): 4758. http://dx.doi.org/10.1109/tsp.2021.3104727.
Texto completoKadambari, Sai Kiran y Sundeep Prabhakar Chepuri. "Product Graph Learning From Multi-Domain Data With Sparsity and Rank Constraints". IEEE Transactions on Signal Processing 69 (2021): 5665–80. http://dx.doi.org/10.1109/tsp.2021.3115947.
Texto completoLiu, Guohua y Jianchun Duan. "RGB-D image segmentation using superpixel and multi-feature fusion graph theory". Signal, Image and Video Processing 14, n.º 6 (17 de febrero de 2020): 1171–79. http://dx.doi.org/10.1007/s11760-020-01647-x.
Texto completoGU, JIANPING, LI ZHANG y CUN CHENG. "DYNAMIC GRAPH MERGING FOR IMAGE SEGMENTATION". International Journal of Wavelets, Multiresolution and Information Processing 11, n.º 06 (noviembre de 2013): 1350051. http://dx.doi.org/10.1142/s0219691313500513.
Texto completoSun, Ning, Ling Leng, Jixin Liu y Guang Han. "Multi-stream slowFast graph convolutional networks for skeleton-based action recognition". Image and Vision Computing 109 (mayo de 2021): 104141. http://dx.doi.org/10.1016/j.imavis.2021.104141.
Texto completoCao, Pingping, Pengpeng Chen y Qiang Niu. "Multi-label image recognition with two-stream dynamic graph convolution networks". Image and Vision Computing 113 (septiembre de 2021): 104238. http://dx.doi.org/10.1016/j.imavis.2021.104238.
Texto completoWan, Jianwu, Liang Niu, Bing Bai y Hongyuan Wang. "Graph Regularized Deep Discrete Hashing for Multi-Label Image Retrieval". IEEE Signal Processing Letters 27 (2020): 1994–98. http://dx.doi.org/10.1109/lsp.2020.3034538.
Texto completoHuang, Shudong, Zhao Kang, Ivor W. Tsang y Zenglin Xu. "Auto-weighted multi-view clustering via kernelized graph learning". Pattern Recognition 88 (abril de 2019): 174–84. http://dx.doi.org/10.1016/j.patcog.2018.11.007.
Texto completoYe, Xulun y Jieyu Zhao. "Multi-manifold clustering: A graph-constrained deep nonparametric method". Pattern Recognition 93 (septiembre de 2019): 215–27. http://dx.doi.org/10.1016/j.patcog.2019.04.029.
Texto completoGu, Xianbin y Jeremiah D. Deng. "A multi-feature bipartite graph ensemble for image segmentation". Pattern Recognition Letters 131 (marzo de 2020): 98–104. http://dx.doi.org/10.1016/j.patrec.2019.12.017.
Texto completoSaboksayr, Seyed Saman, Gonzalo Mateos y Mujdat Cetin. "Online discriminative graph learning from multi-class smooth signals". Signal Processing 186 (septiembre de 2021): 108101. http://dx.doi.org/10.1016/j.sigpro.2021.108101.
Texto completoFrishman, Yaniv y Ayellet Tal. "Multi-Level Graph Layout on the GPU". IEEE Transactions on Visualization and Computer Graphics 13, n.º 6 (noviembre de 2007): 1310–19. http://dx.doi.org/10.1109/tvcg.2007.70580.
Texto completoLi, Chaoyue, Lian Zou, Cien Fan, Hao Jiang y Yifeng Liu. "Multi-Stage Attention-Enhanced Sparse Graph Convolutional Network for Skeleton-Based Action Recognition". Electronics 10, n.º 18 (8 de septiembre de 2021): 2198. http://dx.doi.org/10.3390/electronics10182198.
Texto completoCarrillo, Rafael E., Martin Leblanc, Baptiste Schubnel, Renaud Langou, Cyril Topfel y Pierre-Jean Alet. "High-Resolution PV Forecasting from Imperfect Data: A Graph-Based Solution". Energies 13, n.º 21 (3 de noviembre de 2020): 5763. http://dx.doi.org/10.3390/en13215763.
Texto completoDeng, Cheng, Rongrong Ji, Dacheng Tao, Xinbo Gao y Xuelong Li. "Weakly Supervised Multi-Graph Learning for Robust Image Reranking". IEEE Transactions on Multimedia 16, n.º 3 (abril de 2014): 785–95. http://dx.doi.org/10.1109/tmm.2014.2298841.
Texto completoHu, Lingyue, Kailong Zhao, Bingo Wing-Kuen Ling y Yuxin Lin. "Activity recognition via correlation coefficients based graph with nodes updated by multi-aggregator approach". Biomedical Signal Processing and Control 79 (enero de 2023): 104255. http://dx.doi.org/10.1016/j.bspc.2022.104255.
Texto completoYu, Tianhang, Minjian Zhao, Jie Zhong, Jian Zhang y Pei Xiao. "Low‐complexity graph‐based turbo equalisation for single‐carrier and multi‐carrier FTN signalling". IET Signal Processing 11, n.º 7 (septiembre de 2017): 838–45. http://dx.doi.org/10.1049/iet-spr.2016.0251.
Texto completoCheng, Dawei, Fangzhou Yang, Sheng Xiang y Jin Liu. "Financial time series forecasting with multi-modality graph neural network". Pattern Recognition 121 (enero de 2022): 108218. http://dx.doi.org/10.1016/j.patcog.2021.108218.
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