Academic literature on the topic 'Exact recovery'
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Journal articles on the topic "Exact recovery"
Andrecut, M. "Exact Fourier spectrum recovery." Physics Letters A 377, no. 1-2 (December 2012): 1–6. http://dx.doi.org/10.1016/j.physleta.2012.10.018.
Full textTsuda, Seiya, Yuji Iwahori, M. K. Bhuyan, Robert J. Woodham, and Kunio Kasugai. "Recovering 3D Shape with Absolute Size from Endoscope Images Using RBF Neural Network." International Journal of Biomedical Imaging 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/109804.
Full textCheded, L. "Exact recovery of higher order moments." IEEE Transactions on Information Theory 44, no. 2 (March 1998): 851–58. http://dx.doi.org/10.1109/18.661534.
Full textBerthet, Quentin, Philippe Rigollet, and Piyush Srivastava. "Exact recovery in the Ising blockmodel." Annals of Statistics 47, no. 4 (August 2019): 1805–34. http://dx.doi.org/10.1214/17-aos1620.
Full textDym, Nadav, and Yaron Lipman. "Exact Recovery with Symmetries for Procrustes Matching." SIAM Journal on Optimization 27, no. 3 (January 2017): 1513–30. http://dx.doi.org/10.1137/16m1078628.
Full textAbbe, Emmanuel, Afonso S. Bandeira, and Georgina Hall. "Exact Recovery in the Stochastic Block Model." IEEE Transactions on Information Theory 62, no. 1 (January 2016): 471–87. http://dx.doi.org/10.1109/tit.2015.2490670.
Full textDuval, Vincent, and Gabriel Peyré. "Exact Support Recovery for Sparse Spikes Deconvolution." Foundations of Computational Mathematics 15, no. 5 (October 9, 2014): 1315–55. http://dx.doi.org/10.1007/s10208-014-9228-6.
Full textYou, Qing Shan, and Qun Wan. "Principal Component Pursuit with Weighted Nuclear Norm." Applied Mechanics and Materials 513-517 (February 2014): 1722–26. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.1722.
Full textChen, Xiaohui, and Yun Yang. "Cutoff for Exact Recovery of Gaussian Mixture Models." IEEE Transactions on Information Theory 67, no. 6 (June 2021): 4223–38. http://dx.doi.org/10.1109/tit.2021.3063155.
Full textHajek, Bruce, Yihong Wu, and Jiaming Xu. "Achieving Exact Cluster Recovery Threshold via Semidefinite Programming." IEEE Transactions on Information Theory 62, no. 5 (May 2016): 2788–97. http://dx.doi.org/10.1109/tit.2016.2546280.
Full textDissertations / Theses on the topic "Exact recovery"
Trede, Dennis. "Inverse problems with sparsity constraints convergence rates and exact recovery." Berlin Logos-Verl, 2010. http://d-nb.info/1002361532/04.
Full textFlinth, Axel [Verfasser], Gitta [Akademischer Betreuer] Kutyniok, Gitta [Gutachter] Kutyniok, Rémi [Gutachter] Gribonval, and Felix [Gutachter] Krahmer. "Exact and soft recovery of structured signals from atomic and total variation norm regularization / Axel Flinth ; Gutachter: Gitta Kutyniok, Rémi Gribonval, Felix Krahmer ; Betreuer: Gitta Kutyniok." Berlin : Technische Universität Berlin, 2018. http://d-nb.info/1172809291/34.
Full textNguyen, Thi Thanh. "Algorithmes gloutons orthogonaux sous contrainte de positivité." Thesis, Université de Lorraine, 2019. http://www.theses.fr/2019LORR0133/document.
Full textNon-negative sparse approximation arises in many applications fields such as biomedical engineering, fluid mechanics, astrophysics, and remote sensing. Some classical sparse algorithms can be straightforwardly adapted to deal with non-negativity constraints. On the contrary, the non-negative extension of orthogonal greedy algorithms is a challenging issue since the unconstrained least square subproblems are replaced by non-negative least squares subproblems which do not have closed-form solutions. In the literature, non-negative orthogonal greedy (NNOG) algorithms are often considered to be slow. Moreover, some recent works exploit approximate schemes to derive efficient recursive implementations. In this thesis, NNOG algorithms are introduced as heuristic solvers dedicated to L0 minimization under non-negativity constraints. It is first shown that the latter L0 minimization problem is NP-hard. The second contribution is to propose a unified framework on NNOG algorithms together with an exact and fast implementation, where the non-negative least-square subproblems are solved using the active-set algorithm with warm start initialisation. The proposed implementation significantly reduces the cost of NNOG algorithms and appears to be more advantageous than existing approximate schemes. The third contribution consists of a unified K-step exact support recovery analysis of NNOG algorithms when the mutual coherence of the dictionary is lower than 1/(2K-1). This is the first analysis of this kind
Denoyelle, Quentin. "Theoretical and Numerical Analysis of Super-Resolution Without Grid." Thesis, Paris Sciences et Lettres (ComUE), 2018. http://www.theses.fr/2018PSLED030/document.
Full textThis thesis studies the noisy sparse spikes super-resolution problem for positive measures using the BLASSO, an infinite dimensional convex optimization problem generalizing the LASSO to measures. First, we show that the support stability of the BLASSO for N clustered spikes is governed by an object called the (2N-1)-vanishing derivatives pre-certificate. When it is non-degenerate, solving the BLASSO leads to exact support recovery of the initial measure, in a low noise regime whose size is controlled by the minimal separation distance of the spikes. In a second part, we propose the Sliding Frank-Wolfe algorithm, based on the Frank-Wolfe algorithm with an added step moving continuously the amplitudes and positions of the spikes, that solves the BLASSO. We show that, under mild assumptions, it converges in a finite number of iterations. We apply this algorithm to the 3D fluorescent microscopy problem by comparing three models based on the PALM/STORM technics
Godeme, Jean-Jacques. "Ρhase retrieval with nοn-Euclidean Bregman based geοmetry." Electronic Thesis or Diss., Normandie, 2024. http://www.theses.fr/2024NORMC214.
Full textIn this work, we investigate the phase retrieval problem of real-valued signals in finite dimension, a challenge encountered across various scientific and engineering disciplines. It explores two complementary approaches: retrieval with and without regularization. In both settings, our work is focused on relaxing the Lipschitz-smoothness assumption generally required by first-order splitting algorithms, and which is not valid for phase retrieval cast as a minimization problem. The key idea here is to replace the Euclidean geometry by a non-Euclidean Bregman divergence associated to an appropriate kernel. We use a Bregman gradient/mirror descent algorithm with this divergence to solve thephase retrieval problem without regularization, and we show exact (up to a global sign) recovery both in a deterministic setting and with high probability for a sufficient number of random measurements (Gaussian and Coded Diffraction Patterns). Furthermore, we establish the robustness of this approachagainst small additive noise. Shifting to regularized phase retrieval, we first develop and analyze an Inertial Bregman Proximal Gradient algorithm for minimizing the sum of two functions in finite dimension, one of which is convex and possibly nonsmooth and the second is relatively smooth in the Bregman geometry. We provide both global and local convergence guarantees for this algorithm. Finally, we study noiseless and stable recovery of low complexity regularized phase retrieval. For this, weformulate the problem as the minimization of an objective functional involving a nonconvex smooth data fidelity term and a convex regularizer promoting solutions conforming to some notion of low-complexity related to their nonsmoothness points. We establish conditions for exact and stable recovery and provide sample complexity bounds for random measurements to ensure that these conditions hold. These sample bounds depend on the low complexity of the signals to be recovered. Our new results allow to go far beyond the case of sparse phase retrieval
Nguyen, Thi Thanh. "Algorithmes gloutons orthogonaux sous contrainte de positivité." Electronic Thesis or Diss., Université de Lorraine, 2019. http://www.theses.fr/2019LORR0133.
Full textNon-negative sparse approximation arises in many applications fields such as biomedical engineering, fluid mechanics, astrophysics, and remote sensing. Some classical sparse algorithms can be straightforwardly adapted to deal with non-negativity constraints. On the contrary, the non-negative extension of orthogonal greedy algorithms is a challenging issue since the unconstrained least square subproblems are replaced by non-negative least squares subproblems which do not have closed-form solutions. In the literature, non-negative orthogonal greedy (NNOG) algorithms are often considered to be slow. Moreover, some recent works exploit approximate schemes to derive efficient recursive implementations. In this thesis, NNOG algorithms are introduced as heuristic solvers dedicated to L0 minimization under non-negativity constraints. It is first shown that the latter L0 minimization problem is NP-hard. The second contribution is to propose a unified framework on NNOG algorithms together with an exact and fast implementation, where the non-negative least-square subproblems are solved using the active-set algorithm with warm start initialisation. The proposed implementation significantly reduces the cost of NNOG algorithms and appears to be more advantageous than existing approximate schemes. The third contribution consists of a unified K-step exact support recovery analysis of NNOG algorithms when the mutual coherence of the dictionary is lower than 1/(2K-1). This is the first analysis of this kind
Afdideh, Fardin. "Block-sparse models in multi-modality : application to the inverse model in EEG/MEG." Thesis, Université Grenoble Alpes (ComUE), 2018. http://www.theses.fr/2018GREAT074/document.
Full textThree main challenges have been addressed in this thesis, in three chapters.First challenge is about the ineffectiveness of some classic methods in high-dimensional problems. This challenge is partially addressed through the idea of clustering the coherent parts of a dictionary based on the proposed characterisation, in order to create more incoherent atomic entities in the dictionary, which is proposed as a block structure identification framework. The more incoherent atomic entities, the more improvement in the exact recovery conditions. In addition, we applied the mentioned clustering idea to real-world EEG/MEG leadfields to segment the brain source space, without using any information about the brain sources activity and EEG/MEG signals. Second challenge raises when classic recovery conditions cannot be established for the new concept of constraint, i.e., block-sparsity. Therefore, as the second research orientation, we developed a general framework for block-sparse exact recovery conditions, i.e., four theoretical and one algorithmic-dependent conditions, which ensure the uniqueness of the block-sparse solution of corresponding weighted mixed-norm optimisation problem in an underdetermined system of linear equations. The mentioned generality of the framework is in terms of the properties of the underdetermined system of linear equations, extracted dictionary characterisations, optimisation problems, and ultimately the recovery conditions. Finally, the combination of different information of a same phenomenon is the subject of the third challenge, which is addressed in the last part of dissertation with application to brain source space segmentation. More precisely, we showed that by combining the EEG and MEG leadfields and gaining the electromagnetic properties of the head, more refined brain regions appeared
Bertin, Karine. "Estimation asymptotiquement exacte en norme sup de fonctions multidimensionnelles." Phd thesis, Université Pierre et Marie Curie - Paris VI, 2004. http://tel.archives-ouvertes.fr/tel-00008028.
Full textGaïffas, Stéphane. "Régression non-paramétrique et information spatialement inhomogène." Paris 7, 2005. https://tel.archives-ouvertes.fr/tel-00011261.
Full textGaiffas, Stéphane. "Régression non-paramétrique et information spatialement inhomogène." Phd thesis, Université Paris-Diderot - Paris VII, 2005. http://tel.archives-ouvertes.fr/tel-00011261.
Full textdonnées bruitées spatialement inhomogènes (données dont la quantité
varie sur le domaine d'estimation). Le prototype d'étude est le modèle
de régression avec design aléatoire. Notre objectif est de comprendre
les conséquences du caractère inhomogène des données sur le problème
d'estimation dans le cadre d'étude minimax. Nous adoptons deux points
de vue : local et global. Du point de vue local, nous nous intéressons
à l'estimation de la régression en un point avec peu ou beaucoup de
données. En traduisant cette propriété par différentes hypothèses sur
le comportement local de la densité du design, nous obtenons toute une
gamme de nouvelles vitesses minimax ponctuelles, comprenant des
vitesses très lentes et des vitesses très rapides. Puis, nous
construisons une procédure adaptative en la régularité de la
régression, et nous montrons qu'elle converge avec la vitesse minimax
à laquelle s'ajoute un coût minimal pour l'adaptation locale. Du point
de vue global, nous nous intéressons à l'estimation de la régression
en perte uniforme. Nous proposons des estimateurs qui convergent avec
des vitesses dépendantes de l'espace, lesquelles rendent compte du
caractère inhomogène de l'information dans le modèle. Nous montrons
l'optimalité spatiale de ces vitesses, qui consiste en un renforcement
de la borne inférieure minimax classique pour la perte uniforme. Nous
construisons notamment un estimateur asymptotiquement exact sur une
boule de Hölder de régularité quelconque, ainsi qu'une bande de
confiance dont la largeur s'adapte à la quantité locale de données.
Books on the topic "Exact recovery"
Prasad, Konasale M. Course, Prognosis, and Outcomes of Schizophrenia and Related Disorders. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199331505.003.0004.
Full textVance, Jim. Triathlon 2.0. Human Kinetics, 2016. http://dx.doi.org/10.5040/9781718219298.
Full textMann, Barbara Alice, ed. Daughters of Mother Earth. Greenwood Publishing Group, Inc., 2006. http://dx.doi.org/10.5040/9798400637902.
Full textMashhoon, Bahram. Linearized Nonlocal Gravity. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198803805.003.0007.
Full textBook chapters on the topic "Exact recovery"
Gao, Zheng, and Stilian Stoev. "Exact Support Recovery Under Dependence." In Concentration of Maxima and Fundamental Limits in High-Dimensional Testing and Inference, 47–61. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80964-5_4.
Full textAvrachenkov, Konstantin, and Maximilien Dreveton. "Almost Exact Recovery in Label Spreading." In Lecture Notes in Computer Science, 30–43. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-25070-6_3.
Full textGaudio, Julia, Xiaochun Niu, and Ermin Wei. "Exact Community Recovery in the Geometric SBM." In Proceedings of the 2024 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), 2158–84. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2024. http://dx.doi.org/10.1137/1.9781611977912.78.
Full textZhu, Binhai. "Efficient Exact and Approximate Algorithms for the Complement of Maximal Strip Recovery." In Algorithmic Aspects in Information and Management, 325–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14355-7_33.
Full textBelkić, Dževad, and Karen Belkić. "Exact quantum-mechanical, Padé-based recovery of spectral parameters." In Signal Processing in Magnetic Resonance Spectroscopy with Biomedical Applications, 85–148. CRC Press, 2010. http://dx.doi.org/10.1201/9781439806456-3.
Full text"Exact Quantum-Mechanical, Pade-Based Recovery of Spectral Parameters." In Signal Processing in Magnetic Resonance Spectroscopy with Biomedical Applications, 85–148. CRC Press, 2010. http://dx.doi.org/10.1201/9781439806456-c3.
Full textSilverman, Michael J. "Treatments and illness management and recovery." In Music Therapy in Mental Health for Illness Management and Recovery, 21–57. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780198865285.003.0002.
Full textBoughton, James M. "Planning for a Stable Postwar Recovery, 1941–42." In Harry White and the American Creed, 141–53. Yale University Press, 2021. http://dx.doi.org/10.12987/yale/9780300253795.003.0010.
Full textThurtell, Matthew J., and Robert L. Tomsak. "Nonarteritic Ischemic Optic Neuropathy." In Neuro-Ophthalmology, edited by Matthew J. Thurtell and Robert L. Tomsak, 15–20. Oxford University Press, 2019. http://dx.doi.org/10.1093/med/9780190603953.003.0003.
Full textBack, Anthony. "Cancer." In Managing Death in the ICU, 301–10. Oxford University PressNew York, NY, 2000. http://dx.doi.org/10.1093/oso/9780195128819.003.0023.
Full textConference papers on the topic "Exact recovery"
Atac, Meryem, and Altan Kayran. "Comparative Study of Finite Element Analysis and Geometrically Exact Beam Analysis of a Composite Helicopter Blade." In Vertical Flight Society 72nd Annual Forum & Technology Display, 1–10. The Vertical Flight Society, 2016. http://dx.doi.org/10.4050/f-0072-2016-11536.
Full textLi, Ping, and Cun-Hui Zhang. "Exact sparse recovery with L0 projections." In KDD' 13: The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2013. http://dx.doi.org/10.1145/2487575.2487694.
Full textFu, M. "Exact, optimal, and partial loop transfer recovery." In 29th IEEE Conference on Decision and Control. IEEE, 1990. http://dx.doi.org/10.1109/cdc.1990.203936.
Full textKudo, Hiroyuki, and Tsuneo Saito. "Feasible Cone Beam Scanning Methods for Exact 3-D Tomographic Image Reconstruction." In Signal Recovery and Synthesis. Washington, D.C.: Optica Publishing Group, 1989. http://dx.doi.org/10.1364/srs.1989.fd3.
Full textLu, Canyi, Jiashi Feng, Zhouchen Lin, and Shuicheng Yan. "Exact Low Tubal Rank Tensor Recovery from Gaussian Measurements." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/347.
Full textHajek, Bruce, Yihong Wu, and Jiaming Xu. "Achieving exact cluster recovery threshold via semidefinite programming." In 2015 IEEE International Symposium on Information Theory (ISIT). IEEE, 2015. http://dx.doi.org/10.1109/isit.2015.7282694.
Full textLi, Qiang, Wing-Kin Ma, and Qiong Wu. "Hyperspectral Super-Resolution: Exact Recovery In Polynomial Time." In 2018 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2018. http://dx.doi.org/10.1109/ssp.2018.8450697.
Full textLotfi, Mahsa, and Mathukumalli Vidyasagar. "Exact recovery of sparse signals from binary measurements." In 2018 Indian Control Conference (ICC). IEEE, 2018. http://dx.doi.org/10.1109/indiancc.2018.8307958.
Full textHajek, Bruce, Yihong Wu, and Jiaming Xu. "Exact recovery threshold in the binary censored block model." In 2015 IEEE Information Theory Workshop - Fall (ITW). IEEE, 2015. http://dx.doi.org/10.1109/itwf.2015.7360742.
Full textJin, Yuzhe, Young-Han Kim, and Bhaskar D. Rao. "Performance tradeoffs for exact support recovery of sparse signals." In 2010 IEEE International Symposium on Information Theory - ISIT. IEEE, 2010. http://dx.doi.org/10.1109/isit.2010.5513492.
Full textReports on the topic "Exact recovery"
Barg, Rivka, Kendal D. Hirschi, Avner Silber, Gozal Ben-Hayyim, Yechiam Salts, and Marla Binzel. Combining Elevated Levels of Membrane Fatty Acid Desaturation and Vacuolar H+ -pyrophosphatase Activity for Improved Drought Tolerance. United States Department of Agriculture, December 2012. http://dx.doi.org/10.32747/2012.7613877.bard.
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