Bibliografie tematiche / Sparse and low-rank decomposition

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Letteratura scientifica selezionata sul tema "Sparse and low-rank decomposition"

Autore: Grafiati
Pubblicato: 4 giugno 2021
Ultima modifica: 16 febbraio 2022

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Articoli di riviste sul tema "Sparse and low-rank decomposition"

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Ong, Frank, and Michael Lustig. "Beyond Low Rank + Sparse: Multiscale Low Rank Matrix Decomposition." IEEE Journal of Selected Topics in Signal Processing 10, no. 4 (2016): 672–87. http://dx.doi.org/10.1109/jstsp.2016.2545518.

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Yin, Jingwei, Bing Liu, Guangping Zhu, and Zhinan Xie. "Moving Target Detection Using Dynamic Mode Decomposition." Sensors 18, no. 10 (2018): 3461. http://dx.doi.org/10.3390/s18103461.

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Abstract (sommario):
It is challenging to detect a moving target in the reverberant environment for a long time. In recent years, a kind of method based on low-rank and sparse theory was developed to study this problem. The multiframe data containing the target echo and reverberation are arranged in a matrix, and then, the detection is achieved by low-rank and sparse decomposition of the data matrix. In this paper, we introduce a new method for the matrix decomposition using dynamic mode decomposition (DMD). DMD is usually used to calculate eigenmodes of an approximate linear model. We divided the eigenmodes into
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Rahmani, Mostafa, and George K. Atia. "High Dimensional Low Rank Plus Sparse Matrix Decomposition." IEEE Transactions on Signal Processing 65, no. 8 (2017): 2004–19. http://dx.doi.org/10.1109/tsp.2017.2649482.

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Chartrand, R. "Nonconvex Splitting for Regularized Low-Rank + Sparse Decomposition." IEEE Transactions on Signal Processing 60, no. 11 (2012): 5810–19. http://dx.doi.org/10.1109/tsp.2012.2208955.

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Liu, Jingjing, Donghui He, Xiaoyang Zeng, et al. "ManiDec: Manifold Constrained Low-Rank and Sparse Decomposition." IEEE Access 7 (2019): 112939–52. http://dx.doi.org/10.1109/access.2019.2935235.

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Rong, Kaixuan, Licheng Jiao, Shuang Wang, and Fang Liu. "Pansharpening Based on Low-Rank and Sparse Decomposition." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7, no. 12 (2014): 4793–805. http://dx.doi.org/10.1109/jstars.2014.2347072.

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Zhao, Jingtao, Caixia Yu, Suping Peng, and Chuangjian Li. "3D diffraction imaging method using low-rank matrix decomposition." GEOPHYSICS 85, no. 1 (2020): S1—S10. http://dx.doi.org/10.1190/geo2018-0417.1.

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Abstract (sommario):
Seismic weak responses from subsurface small-scale geologic discontinuities or inhomogeneities are encoded in 3D diffractions. Separating weak diffractions from a strong reflection background is a difficult problem for diffraction imaging, especially for the 3D case when they are tangent to or interfering with each other. Most conventional diffraction separation methods ignore the azimuth discrepancy between reflections and diffractions when suppressing reflections. In fact, the reflections associated with a specific pair of azimuth-dip angle possess sparse characteristics, and the diffraction
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Huang, Jianjun, Xiongwei Zhang, Yafei Zhang, Xia Zou, and Li Zeng. "Speech Denoising via Low-Rank and Sparse Matrix Decomposition." ETRI Journal 36, no. 1 (2014): 167–70. http://dx.doi.org/10.4218/etrij.14.0213.0033.

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Zhang, He, and Vishal M. Patel. "Convolutional Sparse and Low-Rank Coding-Based Image Decomposition." IEEE Transactions on Image Processing 27, no. 5 (2018): 2121–33. http://dx.doi.org/10.1109/tip.2017.2786469.

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Gong, Wenyong, Weihong Xu, Leqin Wu, Xiaohua Xie, and Zhanglin Cheng. "Intrinsic Image Sequence Decomposition Using Low-Rank Sparse Model." IEEE Access 7 (2019): 4024–30. http://dx.doi.org/10.1109/access.2018.2888946.

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Tesi sul tema "Sparse and low-rank decomposition"

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Ebadi, Salehe Erfanian. "Robust subspace estimation via low-rank and sparse decomposition and applications in computer vision." Thesis, Queen Mary, University of London, 2018. http://qmro.qmul.ac.uk/xmlui/handle/123456789/31790.

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Abstract (sommario):
Recent advances in robust subspace estimation have made dimensionality reduction and noise and outlier suppression an area of interest for research, along with continuous improvements in computer vision applications. Due to the nature of image and video signals that need a high dimensional representation, often storage, processing, transmission, and analysis of such signals is a difficult task. It is therefore desirable to obtain a low-dimensional representation for such signals, and at the same time correct for corruptions, errors, and outliers, so that the signals could be readily used for l
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Cordolino, Sobral Andrews. "Robust low-rank and sparse decomposition for moving object detection : from matrices to tensors." Thesis, La Rochelle, 2017. http://www.theses.fr/2017LAROS007/document.

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Dans ce manuscrit de thèse, nous introduisons les avancées récentes sur la décomposition en matrices (et tenseurs) de rang faible et parcimonieuse ainsi que les contributions pour faire face aux principaux problèmes dans ce domaine. Nous présentons d’abord un aperçu des méthodes matricielles et tensorielles les plus récentes ainsi que ses applications sur la modélisation d’arrière-plan et la segmentation du premier plan. Ensuite, nous abordons le problème de l’initialisation du modèle de fond comme un processus de reconstruction à partir de données manquantes ou corrompues. Une nouvelle méthod
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Oreifej, Omar. "Robust Subspace Estimation Using Low-Rank Optimization. Theory and Applications in Scene Reconstruction, Video Denoising, and Activity Recognition." Doctoral diss., University of Central Florida, 2013. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/5684.

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In this dissertation, we discuss the problem of robust linear subspace estimation using low-rank optimization and propose three formulations of it. We demonstrate how these formulations can be used to solve fundamental computer vision problems, and provide superior performance in terms of accuracy and running time. Consider a set of observations extracted from images (such as pixel gray values, local features, trajectories...etc). If the assumption that these observations are drawn from a liner subspace (or can be linearly approximated) is valid, then the goal is to represent each observati
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Bomma, Sushma. "Sparse and low rank approximations for action recognition." Thesis, Heriot-Watt University, 2016. http://hdl.handle.net/10399/3189.

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Abstract (sommario):
Action recognition is crucial area of research in computer vision with wide range of applications in surveillance, patient-monitoring systems, video indexing, Human- Computer Interaction and many more. These applications require automated action recognition. Robust classification methods are sought-after despite influential research in this field over past decade. The data resources have grown tremendously owing to the advances in the digital revolution which cannot be compared to the meagre resources in the past. The main limitation on a system when dealing with video data is the computationa
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Kang, Zhao. "LOW RANK AND SPARSE MODELING FOR DATA ANALYSIS." OpenSIUC, 2017. https://opensiuc.lib.siu.edu/dissertations/1366.

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Abstract (sommario):
Nowadays, many real-world problems must deal with collections of high-dimensional data. High dimensional data usually have intrinsic low-dimensional representations, which are suited for subsequent analysis or processing. Therefore, finding low-dimensional representations is an essential step in many machine learning and data mining tasks. Low-rank and sparse modeling are emerging mathematical tools dealing with uncertainties of real-world data. Leveraging on the underlying structure of data, low-rank and sparse modeling approaches have achieved impressive performance in many data analysis tas
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Sundin, Martin. "Bayesian methods for sparse and low-rank matrix problems." Doctoral thesis, KTH, Signalbehandling, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-191139.

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Many scientific and engineering problems require us to process measurements and data in order to extract information. Since we base decisions on information,it is important to design accurate and efficient processing algorithms. This is often done by modeling the signal of interest and the noise in the problem. One type ofmodeling is Compressed Sensing, where the signal has a sparse or low-rank representation. In this thesis we study different approaches to designing algorithms for sparse and low-rank problems. Greedy methods are fast methods for sparse problems which iteratively detects and e
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Lou, Jian. "Study on efficient sparse and low-rank optimization and its applications." HKBU Institutional Repository, 2018. https://repository.hkbu.edu.hk/etd_oa/543.

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Abstract (sommario):
Sparse and low-rank models have been becoming fundamental machine learning tools and have wide applications in areas including computer vision, data mining, bioinformatics and so on. It is of vital importance, yet of great difficulty, to develop efficient optimization algorithms for solving these models, especially under practical design considerations of computational, communicational and privacy restrictions for ever-growing larger scale problems. This thesis proposes a set of new algorithms to improve the efficiency of the sparse and low-rank models optimization. First, facing a large numbe
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Shi, Qiquan. "Low rank tensor decomposition for feature extraction and tensor recovery." HKBU Institutional Repository, 2018. https://repository.hkbu.edu.hk/etd_oa/549.

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Abstract (sommario):
Feature extraction and tensor recovery problems are important yet challenging, particularly for multi-dimensional data with missing values and/or noise. Low-rank tensor decomposition approaches are widely used for solving these problems. This thesis focuses on three common tensor decompositions (CP, Tucker and t-SVD) and develops a set of decomposition-based approaches. The proposed methods aim to extract low-dimensional features from complete/incomplete data and recover tensors given partial and/or grossly corrupted observations.;Based on CP decomposition, semi-orthogonal multilinear principa
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Primadhanty, Audi. "Low-rank regularization for high-dimensional sparse conjunctive feature spaces in information extraction." Doctoral thesis, Universitat Politècnica de Catalunya, 2017. http://hdl.handle.net/10803/461682.

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Abstract (sommario):
One of the challenges in Natural Language Processing (NLP) is the unstructured nature of texts, in which useful information is not easily identifiable. Information Extraction (IE) aims to alleviate it by enabling automatic extraction of structured information from such text sources. The resulting structured information will facilitate easier querying, organizing, and analyzing of data from texts. In this thesis, we are interested in two IE related tasks: (i) named entity classification and (ii) template filling. Specifically, this thesis examines the problem of learning classifiers of text sp
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Wang, Tianming. "Non-convex methods for spectrally sparse signal reconstruction via low-rank Hankel matrix completion." Diss., University of Iowa, 2018. https://ir.uiowa.edu/etd/6331.

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Abstract (sommario):
Spectrally sparse signals arise in many applications of signal processing. A spectrally sparse signal is a mixture of a few undamped or damped complex sinusoids. An important problem from practice is to reconstruct such a signal from partial time domain samples. Previous convex methods have the drawback that the computation and storage costs do not scale well with respect to the signal length. This common drawback restricts their applicabilities to large and high-dimensional signals. The reconstruction of a spectrally sparse signal from partial samples can be formulated as a low-rank Hankel ma
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Libri sul tema "Sparse and low-rank decomposition"

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Fu, Yun, ed. Low-Rank and Sparse Modeling for Visual Analysis. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12000-3.

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Bouwmans, Thierry, Necdet Serhat Aybat, and El-hadi Zahzah. Handbook of Robust Low-Rank and Sparse Matrix Decomposition. Taylor & Francis Group, 2020.

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Bouwmans, Thierry, Necdet Serhat Aybat, and El-hadi Zahzah, eds. Handbook of Robust Low-Rank and Sparse Matrix Decomposition. Chapman and Hall/CRC, 2016. http://dx.doi.org/10.1201/b20190.

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Low-rank Decomposition of Multi-dimensional Arrays. CRC Press, 2005.

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Huang, Thomas S. Deep Learning Through Sparse and Low-Rank Modeling. Elsevier Science & Technology, 2019.

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Fu, Yun. Low-Rank and Sparse Modeling for Visual Analysis. Springer, 2016.

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Deep Learning Through Sparse and Low-Rank Modeling. Elsevier, 2019. http://dx.doi.org/10.1016/c2017-0-00154-4.

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Capitoli di libri sul tema "Sparse and low-rank decomposition"

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Fuentes, Victor K., and Jon Lee. "Low-Rank/Sparse-Inverse Decomposition via Woodbury." In Operations Research Proceedings 2016. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55702-1_16.

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Nakajima, Shinichi, Masashi Sugiyama, and S. Babacan. "Bayesian Sparse Estimation for Background/Foreground Separation." In Handbook of Robust Low-Rank and Sparse Matrix Decomposition. CRC Press, 2016. http://dx.doi.org/10.1201/b20190-27.

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Sobral, Andrews, Thierry Bouwmans, and El-Hadi Zahzah. "LRSLibrary: Low-Rank and Sparse Tools for Background Modeling and Subtraction in Videos." In Handbook of Robust Low-Rank and Sparse Matrix Decomposition. CRC Press, 2016. http://dx.doi.org/10.1201/b20190-24.

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Kutz, Jake, Xing Fu, Steven Brunton, and Jacob Grosek. "Dynamic Mode Decomposition for Robust PCA with Applications to Foreground/Background Subtraction in Video Streams and Multi-Resolution Analysis." In Handbook of Robust Low-Rank and Sparse Matrix Decomposition. CRC Press, 2016. http://dx.doi.org/10.1201/b20190-25.

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Javed, Sajid, Seon Oh, Thierry Bouwmans, and Soon Jung. "Stochastic RPCA for Background/Foreground Separation." In Handbook of Robust Low-Rank and Sparse Matrix Decomposition. CRC Press, 2016. http://dx.doi.org/10.1201/b20190-26.

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Guo, Jie, Chunyou Li, Zuojian Zhou, and Jingui Pan. "Reflection Separation Using Patch-Wise Sparse and Low-Rank Decomposition." In Advances in Multimedia Information Processing – PCM 2018. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00776-8_17.

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Zeng, Zinan, Tsung-Han Chan, Kui Jia, and Dong Xu. "Finding Correspondence from Multiple Images via Sparse and Low-Rank Decomposition." In Computer Vision – ECCV 2012. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33715-4_24.

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Peng, Tingying, Lichao Wang, Christine Bayer, Sailesh Conjeti, Maximilian Baust, and Nassir Navab. "Shading Correction for Whole Slide Image Using Low Rank and Sparse Decomposition." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10404-1_5.

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Kong, Wanzeng, Yan Liu, Bei Jiang, Guojun Dai, and Lin Xu. "A New EEG Signal Processing Method Based on Low-Rank and Sparse Decomposition." In Communications in Computer and Information Science. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5230-9_54.

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Zweng, Markus, Pascal Fallavollita, Stefanie Demirci, Markus Kowarschik, Nassir Navab, and Diana Mateus. "Automatic Guide-Wire Detection for Neurointerventions Using Low-Rank Sparse Matrix Decomposition and Denoising." In Augmented Environments for Computer-Assisted Interventions. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24601-7_12.

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Atti di convegni sul tema "Sparse and low-rank decomposition"

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Ong, Frank, and Michael Lustig. "Beyond low rank + sparse: Multi-scale low rank matrix decomposition." In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2016. http://dx.doi.org/10.1109/icassp.2016.7472561.

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Ulfarsson, M. O., V. Solo, and G. Marjanovic. "Sparse and low rank decomposition using l0 penalty." In ICASSP 2015 - 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2015. http://dx.doi.org/10.1109/icassp.2015.7178584.

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Chen, Chongyu, Jianfei Cai, Weisi Lin, and Guangming Shi. "Surveillance video coding via low-rank and sparse decomposition." In the 20th ACM international conference. ACM Press, 2012. http://dx.doi.org/10.1145/2393347.2396294.

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Xue, Yawen, Xiaojie Guo, and Xiaochun Cao. "Motion saliency detection using low-rank and sparse decomposition." In ICASSP 2012 - 2012 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2012. http://dx.doi.org/10.1109/icassp.2012.6288171.

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Fevotte, Cedric, and Matthieu Kowalski. "Hybrid sparse and low-rank time-frequency signal decomposition." In 2015 23rd European Signal Processing Conference (EUSIPCO). IEEE, 2015. http://dx.doi.org/10.1109/eusipco.2015.7362426.

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Rong, Kaixuan, Shuang Wang, Xiaohua Zhang, and Biao Hou. "Low-rank and sparse matrix decomposition-based pan sharpening." In IGARSS 2012 - 2012 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2012. http://dx.doi.org/10.1109/igarss.2012.6351041.

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Huang, Junhao, Weize Sun, Lei Huang, and Shaowu Chen. "Deep Compression with Low Rank and Sparse Integrated Decomposition." In 2019 IEEE 7th International Conference on Computer Science and Network Technology (ICCSNT). IEEE, 2019. http://dx.doi.org/10.1109/iccsnt47585.2019.8962461.

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Zhang, Chunjie, Jing Liu, Qi Tian, Changsheng Xu, Hanqing Lu, and Songde Ma. "Image classification by non-negative sparse coding, low-rank and sparse decomposition." In 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2011. http://dx.doi.org/10.1109/cvpr.2011.5995484.

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Zhang, Lihe, and Chen Ma. "Low-rank, sparse matrix decomposition and group sparse coding for image classification." In 2012 19th IEEE International Conference on Image Processing (ICIP 2012). IEEE, 2012. http://dx.doi.org/10.1109/icip.2012.6466948.

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Chandrasekaran, Venkat, Sujay Sanghavi, Pablo A. Parrilo, and Alan S. Willsky. "Sparse and low-rank matrix decompositions." In 2009 47th Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2009. http://dx.doi.org/10.1109/allerton.2009.5394889.

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Rapporti di organizzazioni sul tema "Sparse and low-rank decomposition"

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Ekambaram, Venkatesan, and Kannan Ramchandran. Cooperative Non-Line-of-Sight Localization Using Low-rank + Sparse Matrix Decomposition. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada561810.

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Doostan, Alireza. Early Career Award: An Enabling Computational Framework for Uncertainty Assimilation and Propagation in Complex PDE Systems: Sparse and Low-rank Techniques (Final Report). Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1511650.

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