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Статті в журналах з теми "Tensor decomposition approach"
Hameduddin, Ismail, Charles Meneveau, Tamer A. Zaki, and Dennice F. Gayme. "Geometric decomposition of the conformation tensor in viscoelastic turbulence." Journal of Fluid Mechanics 842 (March 12, 2018): 395–427. http://dx.doi.org/10.1017/jfm.2018.118.
Повний текст джерелаOuerfelli, Mohamed, Mohamed Tamaazousti, and Vincent Rivasseau. "Random Tensor Theory for Tensor Decomposition." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 7 (June 28, 2022): 7913–21. http://dx.doi.org/10.1609/aaai.v36i7.20761.
Повний текст джерелаZhu, Ben-Chao, and Xiang-Song Chen. "Tensor gauge condition and tensor field decomposition." Modern Physics Letters A 30, no. 35 (October 28, 2015): 1550192. http://dx.doi.org/10.1142/s0217732315501928.
Повний текст джерелаSucharitha, B., and Dr K. Anitha Sheela. "Compression of Hyper Spectral Images using Tensor Decomposition Methods." International Journal of Circuits, Systems and Signal Processing 16 (October 7, 2022): 1148–55. http://dx.doi.org/10.46300/9106.2022.16.138.
Повний текст джерелаFossati, Caroline, Salah Bourennane, Romuald Sabatier, and Antonio Di Giacomo. "Tensorial Model for Photolithography Aerial Image Simulation." Advances in OptoElectronics 2009 (December 6, 2009): 1–9. http://dx.doi.org/10.1155/2009/457549.
Повний текст джерелаKhoromskij, B. N. "Structured Rank-(r1, . . . , rd) Decomposition of Function-related Tensors in R_D." Computational Methods in Applied Mathematics 6, no. 2 (2006): 194–220. http://dx.doi.org/10.2478/cmam-2006-0010.
Повний текст джерелаSobolev, Konstantin, Dmitry Ermilov, Anh-Huy Phan, and Andrzej Cichocki. "PARS: Proxy-Based Automatic Rank Selection for Neural Network Compression via Low-Rank Weight Approximation." Mathematics 10, no. 20 (October 14, 2022): 3801. http://dx.doi.org/10.3390/math10203801.
Повний текст джерелаSchultz, T., and H. P. Seidel. "Estimating Crossing Fibers: A Tensor Decomposition Approach." IEEE Transactions on Visualization and Computer Graphics 14, no. 6 (November 2008): 1635–42. http://dx.doi.org/10.1109/tvcg.2008.128.
Повний текст джерелаFernandes, Sofia, Hadi Fanaee-T, and João Gama. "Dynamic graph summarization: a tensor decomposition approach." Data Mining and Knowledge Discovery 32, no. 5 (July 12, 2018): 1397–420. http://dx.doi.org/10.1007/s10618-018-0583-9.
Повний текст джерелаShi, Qiquan, Jiaming Yin, Jiajun Cai, Andrzej Cichocki, Tatsuya Yokota, Lei Chen, Mingxuan Yuan, and Jia Zeng. "Block Hankel Tensor ARIMA for Multiple Short Time Series Forecasting." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 5758–66. http://dx.doi.org/10.1609/aaai.v34i04.6032.
Повний текст джерелаДисертації з теми "Tensor decomposition approach"
Cavalcante, Ãtalo Vitor. "Tensor approach for channel estimation in MIMO multi-hop cooperative networks." Universidade Federal do CearÃ, 2014. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=12442.
Повний текст джерелаIn this dissertation the problem of channel estimation in cooperative MIMO systems is investigated. More specifically, channel estimation techniques have been developed for a communication system assisted by relays with amplify-and-forward (AF) processing system in a three-hop scenario. The techniques developed use training sequences and enable, at the receiving node, the estimation of all the channels involved in the communication process. In an initial scenario, we consider a communication system with N transmit antennas and M receive antennas and between these nodes we have two relay groups with R1 and R2 antennas each. We propose protocols based on temporal multiplexing to coordinate the retransmission of the signals. At the end of the training phase, the receiving node estimates the channel matrices by combining the received data. By exploiting the multilinear (tensorial) structure of the sets of signals, we can model the received data through tensor models, such as PARAFAC and PARATUCK2 . This work proposes the combined use of these models and algebraic techniques to explore the spatial diversity. Secondly, we consider that the number of transmit and receive antennas at the relays may be different and that the data can travel in a bidirectional scheme (two-way). In order to validate the algorithms we use Monte-Carlo simulations in which we compare our proposed models with competing channel estimation algorithms, such as, the PARAFAC and Khatri-Rao factorization based algorithms in terms of NMSE and bit error rate.
Nesta dissertaÃÃo o problema de estimaÃÃo de canal em sistemas MIMO cooperativos à investigado. Mais especificamente, foram desenvolvidas tÃcnicas para estimaÃÃo de canal em um sistema de comunicaÃÃo assistida por relays com processamento do tipo amplifica-e-encaminha (do inglÃs, amplify-and-forward) em um cenÃrio de 3 saltos. As tÃcnicas desenvolvidas utilizam sequÃncia de treinamento e habilitam, no nà receptor, a estimaÃÃo de todos os canais envolvidos no processo de comunicaÃÃo. Em um cenÃrio inicial, consideramos um sistema de comunicaÃÃo com N antenas transmissoras e M antenas receptoras e entre esses nÃs temos dois grupos de relays com R1 e R2 antenas cada um. Foram desenvolvidos protocolos de transmissÃo baseado em multiplexaÃÃo temporal para coordenar as retransmissÃes dos sinais. Ao final da fase de treinamento, o nà receptor faz a estimaÃÃo das matrizes de canal atravÃs da combinaÃÃo dos dados recebidos. Explorando a estrutura multilinear (tensorial) dos diversos conjuntos de sinais, podemos modelar os dados recebidos atravÃs de modelos tensoriais, tais como: PARAFAC e PARATUCK2. Este trabalho propÃe a utilizaÃÃo combinada desses modelos e de tÃcnicas algÃbricas para explorar a diversidade espacial. Em um segundo momento, consideramos que o nÃmero de antenas transmissoras e receptoras dos relays podem ser diferentes e ainda que os dados podem trafegar em um esquema bidirecional (do inglÃs, two-way). Para fins de validaÃÃo dos algoritmos utilizamos simulaÃÃes de Monte-Carlo em que comparamos os modelos propostos com outros algoritmos de estimaÃÃo de canal, tais como os algoritmos baseados em PARAFAC e FatoraÃÃo de Khatri-Rao em termos de NMSE e taxa de erro de bit.
Vu, Thi Thanh Xuan. "Optimisation déterministe et stochastique pour des problèmes de traitement d'images en grande dimension." Thesis, Aix-Marseille, 2017. http://www.theses.fr/2017AIXM0540.
Повний текст джерелаIn this PhD thesis, we consider the problem of the Canonical Polyadic Decomposition (CPD) of potentially large $N$-th order tensors under different constraints (non-negativity, sparsity due to a possible overestimation of the tensor rank, etc.). To tackle such a problem, we propose three new iterative methods: a standard gradient based deterministic approach, a stochastic approach (memetic) and finally a proximal approach (Block-Coordinate Variable Metric Forward-Backward). The first approach extends J-P. Royer's works to the case of non-negative N-th order tensors. In the stochastic approach, genetic (memetic) methods are considered for the first time to solve the CPD problem. Their general principle is based on the evolution of a family of candidates. In the third type of approaches, a proximal algorithm namely the Block-Coordinate Variable Metric Forward-Backward is presented. The algorithm relies on two main steps: a gradient step and a proximal step. The blocks of coordinates naturally correspond to latent matrices. We propose a majorant function as well as a preconditioner with regard to each block. All methods are compared with other popular algorithms of the literature on synthetic (fluorescence spectroscopy like or random) data and on real experimental data corresponding to a water monitoring campaign aiming at detecting the appearance of pollutants
(9192548), Zongwei Li. "Autoregressive Tensor Decomposition for NYC Taxi Data Analysis." Thesis, 2020.
Знайти повний текст джерелаЧастини книг з теми "Tensor decomposition approach"
Vu, Xuan, Caroline Chaux, Nadège Thirion-Moreau, and Sylvain Maire. "A Proximal Approach for Nonnegative Tensor Decomposition." In Latent Variable Analysis and Signal Separation, 201–10. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53547-0_20.
Повний текст джерелаFan, Jianchao, and Jun Wang. "A Collective Neurodynamic Optimization Approach to Nonnegative Tensor Decomposition." In Advances in Neural Networks - ISNN 2017, 207–13. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59081-3_25.
Повний текст джерелаColace, Francesco, Dajana Conte, Brij Gupta, Domenico Santaniello, Alfredo Troiano, and Carmine Valentino. "A Novel Context-Aware Recommendation Approach Based on Tensor Decomposition." In Proceedings of Seventh International Congress on Information and Communication Technology, 453–62. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1610-6_39.
Повний текст джерелаPhan, Anh Huy, Andrzej Cichocki, Petr Tichavský, Danilo P. Mandic, and Kiyotoshi Matsuoka. "On Revealing Replicating Structures in Multiway Data: A Novel Tensor Decomposition Approach." In Latent Variable Analysis and Signal Separation, 297–305. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28551-6_37.
Повний текст джерелаMironov, Rumen, and Ivo Draganov. "Multidimensional Graphic Objects Filtration Using HoSVD Tensor Decomposition." In New Approaches for Multidimensional Signal Processing, 255–66. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4676-5_21.
Повний текст джерелаDraganov, Ivo, and Rumen Mironov. "Tracking of Domestic Animals in Thermal Videos by Tensor Decompositions." In New Approaches for Multidimensional Signal Processing, 57–71. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4676-5_4.
Повний текст джерелаSewe, Erik, Georg Pangalos, and Gerwald Lichtenberg. "Approaches to Fault Detection for Heating Systems Using CP Tensor Decompositions." In Advances in Intelligent Systems and Computing, 128–52. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01470-4_8.
Повний текст джерелаKountchev, Roumen, and Roumiana Kountcheva. "Hierarchical Decomposition of Third-Order Tensor Through Adaptive Branched Inverse Difference Pyramid Based on 3D-WHT." In New Approaches for Multidimensional Signal Processing, 49–61. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8558-3_3.
Повний текст джерелаDing, Yue, Dong Wang, and Xin Xin. "Novel Approaches for Shop Recommendation in Large Shopping Mall Scenario: From Matrix Factorization to Tensor Decomposition." In Knowledge Science, Engineering and Management, 471–82. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25159-2_42.
Повний текст джерелаChikitkin, Aleksandr V., and Egor K. Kornev. "Different Approaches to Numerical Solution of the Boltzmann Equation with Model Collision Integral Using Tensor Decompositions." In Smart Modelling for Engineering Systems, 105–16. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4619-2_9.
Повний текст джерелаТези доповідей конференцій з теми "Tensor decomposition approach"
Krishnaswamy, Sriram, and Mrinal Kumar. "A Tensor Decomposition Approach to Data Association." In 2018 AIAA Guidance, Navigation, and Control Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2018. http://dx.doi.org/10.2514/6.2018-1134.
Повний текст джерелаMarmin, Arthur, Marc Castella, and Jean-Christophe Pesquet. "A Moment-Based Approach for Guaranteed Tensor Decomposition." In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9054186.
Повний текст джерелаSofuoglu, Seyyid Emre, and Selin Aviyente. "A Two-Stage Approach to Robust Tensor Decomposition." In 2018 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2018. http://dx.doi.org/10.1109/ssp.2018.8450832.
Повний текст джерелаPimentel-Alarcon, Daniel L. "A simpler approach to low-rank tensor canonical polyadic decomposition." In 2016 54th Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2016. http://dx.doi.org/10.1109/allerton.2016.7852269.
Повний текст джерелаLi, Yang, Peng Yu, Luolei Zhang, Jialin Wang, and Jiansheng Wu. "An improved approach on distortion decomposition of magnetotelluric impedance tensor." In SEG Technical Program Expanded Abstracts 2010. Society of Exploration Geophysicists, 2010. http://dx.doi.org/10.1190/1.3513907.
Повний текст джерелаSun, Yifei, and Mrinal Kumar. "A tensor decomposition approach to high dimensional stationary Fokker-Planck equations." In 2014 American Control Conference - ACC 2014. IEEE, 2014. http://dx.doi.org/10.1109/acc.2014.6859175.
Повний текст джерелаZhang, Guoyong, Xiao Fu, Kejun Huang, and Jun Wang. "Hyperspectral Super-Resolution: A Coupled Nonnegative Block-Term Tensor Decomposition Approach." In 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP). IEEE, 2019. http://dx.doi.org/10.1109/camsap45676.2019.9022476.
Повний текст джерелаShukla, Sparsh, Ishita Kalsi, Ayush Jain, and Ankita Verma. "A Tensor Decomposition Based Approach for Context-Aware Recommender Systems (CARS)." In IC3 '21: 2021 Thirteenth International Conference on Contemporary Computing. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3474124.3474191.
Повний текст джерелаSchnur, Jennifer J., Ryan Karl, Angelica Garcia-Martinez, Meng Jiang, and Nitesh V. Chawla. "Imputing Growth Snapshot Similarity in Early Childhood Development: A Tensor Decomposition Approach." In 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2020. http://dx.doi.org/10.1109/bibm49941.2020.9313188.
Повний текст джерелаRibeiro, Lucas N., Antonio R. Hidalgo-Munoz, and Vicente Zarzoso. "Atrial signal extraction in atrial fibrillation electrocardiograms using a tensor decomposition approach." In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2015. http://dx.doi.org/10.1109/embc.2015.7320000.
Повний текст джерелаЗвіти організацій з теми "Tensor decomposition approach"
Dunlavy, Daniel M., Evrim Acar, and Tamara Gibson Kolda. An optimization approach for fitting canonical tensor decompositions. Office of Scientific and Technical Information (OSTI), February 2009. http://dx.doi.org/10.2172/978916.
Повний текст джерела