Готові списки джерел за темами / Sparsity Problem

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Добірка наукової літератури з теми "Sparsity Problem"

Автор: Grafiati

Опубліковано: 3 червня 2025

Оновлено: 31 липня 2025

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Статті в журналах з теми "Sparsity Problem"

Yang, Wei, Lili Pan, and Jinhui Wan. "Smoothing gradient descent algorithm for the composite sparse optimization." AIMS Mathematics 9, no. 12 (2024): 33401–22. http://dx.doi.org/10.3934/math.20241594.

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Composite sparsity generalizes the standard sparsity that considers the sparsity on a linear transformation of the variables. In this paper, we study the composite sparse optimization problem consisting of minimizing the sum of a nondifferentiable loss function and the $ {\mathcal{\ell}_0} $ penalty term of a matrix times the coefficient vector. First, we consider an exact continuous relaxation problem with a capped-$ {\mathcal{\ell}_1} $ penalty that has the same optimal solution as the primal problem. Specifically, we propose the lifted stationary point of the relaxation problem and

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Li, Haiyang, Jigen Peng, and Shigang Yue. "The Sparsity of Underdetermined Linear System vialpMinimization for0." Mathematical Problems in Engineering 2015 (2015): 1–6. http://dx.doi.org/10.1155/2015/584712.

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The sparsity problems have attracted a great deal of attention in recent years, which aim to find the sparsest solution of a representation or an equation. In the paper, we mainly study the sparsity of underdetermined linear system vialpminimization for0<p<1. We show, for a given underdetermined linear system of equationsAm×nX=b, that although it is not certain that the problem(Pp)(i.e.,minXXppsubject toAX=b, where0<p<1) generates sparser solutions as the value ofpdecreases and especially the problem(Pp)generates sparser solutions than the problem(P1)(i.e.,minXX1subject toAX=b), th

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McCormick, S. Thomas, and S. Frank Chang. "The Weighted Sparsity Problem: Complexity and Algorithms." SIAM Journal on Discrete Mathematics 6, no. 1 (1993): 57–69. http://dx.doi.org/10.1137/0406005.

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O'Brien, Thomas S., and Stuart R. Taylor. "The Problem of Sparsity in Education Provision." Urban Studies 25, no. 6 (1988): 520–26. http://dx.doi.org/10.1080/00420988820080681.

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Ren, Xiaozhen, and Yuying Jiang. "Spatial Domain Terahertz Image Reconstruction Based on Dual Sparsity Constraints." Sensors 21, no. 12 (2021): 4116. http://dx.doi.org/10.3390/s21124116.

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Terahertz time domain spectroscopy imaging systems suffer from the problems of long image acquisition time and massive data processing. Reducing the sampling rate will lead to the degradation of the imaging reconstruction quality. To solve this issue, a novel terahertz imaging model, named the dual sparsity constraints terahertz image reconstruction model (DSC-THz), is proposed in this paper. DSC-THz fuses the sparsity constraints of the terahertz image in wavelet and gradient domains into the terahertz image reconstruction model. Differing from the conventional wavelet transform, we introduce

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Dell’Aversano, Angela, Giovanni Leone, and Raffaele Solimene. "Comparing Two Approaches for Point-Like Scatterer Detection." International Journal of Antennas and Propagation 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/139235.

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Many inverse scattering problems concern the detection and localisation of point-like scatterers which are sparsely enclosed within a prescribed investigation domain. Therefore, it looks like a good option to tackle the problem by applying reconstruction methods that are properly tailored for such a type of scatterers or that naturally enforce sparsity in the reconstructions. Accordingly, in this paper we compare the time reversal-MUSIC and the compressed sensing. The study develops through numerical examples and focuses on the role of noise in data and mutual coupling between the scatterers.

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畅, 含笑. "The Solution of Sparsity-Constrained Split Feasibility Problem." Advances in Applied Mathematics 05, no. 02 (2016): 269–75. http://dx.doi.org/10.12677/aam.2016.52034.

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Wang, Guanglei, and Hassan Hijazi. "Exploiting sparsity for the min k-partition problem." Mathematical Programming Computation 12, no. 1 (2019): 109–30. http://dx.doi.org/10.1007/s12532-019-00165-3.

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Xue, Andy Yuan, Jianzhong Qi, Xing Xie, Rui Zhang, Jin Huang, and Yuan Li. "Solving the data sparsity problem in destination prediction." VLDB Journal 24, no. 2 (2014): 219–43. http://dx.doi.org/10.1007/s00778-014-0369-7.

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PLONKA, GERLIND, and JIANWEI MA. "CURVELET-WAVELET REGULARIZED SPLIT BREGMAN ITERATION FOR COMPRESSED SENSING." International Journal of Wavelets, Multiresolution and Information Processing 09, no. 01 (2011): 79–110. http://dx.doi.org/10.1142/s0219691311003955.

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Compressed sensing is a new concept in signal processing. Assuming that a signal can be represented or approximated by only a few suitably chosen terms in a frame expansion, compressed sensing allows one to recover this signal from much fewer samples than the Shannon–Nyquist theory requires. Many images can be sparsely approximated in expansions of suitable frames as wavelets, curvelets, wave atoms and others. Generally, wavelets represent point-like features while curvelets represent line-like features well. For a suitable recovery of images, we propose models that contain weighted sparsity c

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Дисертації з теми "Sparsity Problem"

Flemming, Jens. "Quadratic Inverse Problems and Sparsity Promoting Regularization." Doctoral thesis, Universitätsbibliothek Chemnitz, 2018. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-232402.

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Ill-posed inverse problems with quadratic structure are introduced, studied and solved. As an example an inverse problem appearing in laser optics is solved numerically based on a new regularized inversion algorithm. In addition, the theory of sparsity promoting regularization is extended to situations in which sparsity cannot be expected and also to equations with non-injective operators.

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Trede, Dennis. "Inverse problems with sparsity constraints convergence rates and exact recovery." Berlin Logos-Verl, 2010. http://d-nb.info/1002361532/04.

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Huang, Zan, Hsinchun Chen, and Daniel Zeng. "Applying Associative Retrieval Techniques to Alleviate the Sparsity Problem in Collaborative Filtering." ACM, 2004. http://hdl.handle.net/10150/105493.

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Artificial Intelligence Lab, Department of MIS, University of Arizona Recommender systems are being widely applied in many application settings to suggest products, services, and information items to potential consumers. Collaborative filtering, the most successful recommendation approach, makes recommendations based on past transactions and feedback from consumers sharing similar interests. A major problem limiting the usefulness of collaborative filtering is the sparsity problem, which refers to a situation in which transactional or feedback data is sparse and insufficient to identify sim

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Lamus, Garcia Herreros Camilo. "Models and algorithms of brain connectivity, spatial sparsity, and temporal dynamics for the MEG/EEG inverse problem." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/103160.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, 2015. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 123-131). Magnetoencephalography (MEG) and electroencephalography (EEG) are noninvasive functional neuroimaging techniques that provide high temporal resolution recordings of brain activity, offering a unique means to study fast neural d

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Francisco, André Biasin Segalla. "Esparsidade estruturada em reconstrução de fontes de EEG." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/43/43134/tde-13052018-112615/.

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Neuroimagiologia funcional é uma área da neurociência que visa o desenvolvimento de diversas técnicas para mapear a atividade do sistema nervoso e esteve sob constante desenvolvimento durante as últimas décadas devido à sua grande importância para aplicações clínicas e pesquisa. Técnicas usualmente utilizadas, como imagem por ressonância magnética functional (fMRI) e tomografia por emissão de pósitrons (PET) têm ótima resolução espacial (~ mm), mas uma resolução temporal limitada (~ s), impondo um grande desafio para nossa compreensão a respeito da dinâmica de funções cognitivas mais elevadas,

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Wei, Mu-Hsin. "Electromagnetic induction spectroscopy for the detection of subsurface targets." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/45896.

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This thesis presents a robust method for estimating the relaxations of a metallic object from its electromagnetic induction (EMI) response. The EMI response of a metallic object can be accurately modeled by a sum of real decaying exponentials. However, it is difficult to obtain the model parameters from measurements when the number of exponentials in the sum is unknown or the terms are strongly correlated. Traditionally, the relaxation constants are estimated by nonlinear iterative search that often leads to unsatisfactory results. An effective EMI modeling technique is developed by first lin

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Hernández, Castañón Viviana del Rocío. "Localization of brain oscillatory sources from (S)EEG recordings." Electronic Thesis or Diss., Université de Lorraine, 2023. http://www.theses.fr/2023LORR0010.

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Nous avons abordé dans cette thèse le problème de la localisation des sources oscillatoires, dans le domaine temporel ou en fréquentiel, en considérant des enregistrements EEG et stéréo-EEG (SEEG). L'application visée est l'identification des structures cérébrales répondant aux protocoles de stimulation visuelle périodique rapide (FPVS). Ces protocoles induisent des activités cérébrales à la même fréquence que la stimulation (avec d'éventuelles harmoniques). Ces signaux sont généralement noyées dans le bruit, mais ils apparaissent clairement lors du passage en fréquentiel. Outre cette sparsité

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Carcreff, Ewen. "Déconvolution adaptative pour le contrôle non destructif par ultrasons." Thesis, Le Mans, 2014. http://www.theses.fr/2014LEMA1009/document.

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Nous nous intéressons au contrôle non destructif par ultrasons des matériaux industriels. En pratique, les signaux réceptionnés par le transducteur ultrasonore sont analysés pour détecter les discontinuités de la pièce inspectée. L'analyse est néanmoins rendue difficile par l'acquisition numérique, les effets de la propagation ultrasonore et la superposition des échos lorsque les discontinuités sont proches. La déconvolution parcimonieuse est une méthode inverse qui permet d'aborder ce problème afin de localiser précisément les discontinuités. Ce procédé favorise les signaux parcimonieux, c'es

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Dai, Liang. "On some sparsity related problems and the randomized Kaczmarz algorithm." Licentiate thesis, Uppsala universitet, Avdelningen för systemteknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-226198.

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This thesis studies several problems related to recovery and estimation. Specifically, these problems are about sparsity and low-rankness, and the randomized Kaczmarz algorithm. This thesis includes four papers referred to as Paper A, Paper B, Paper C, and Paper D. Paper A considers how to make use of the fact that the solution to an overdetermined system is sparse. This paper presents a three-stage approach to accomplish the task. We show that this strategy, under the assumptions as made in the paper, achieves the oracle property. In Paper B, a Hankel-matrix completion problem arising in syst

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Carloni, Gertosio Rémi. "Joint deconvolution and semi & unsupervised component separation, with application to radio-interferometry." Electronic Thesis or Diss., université Paris-Saclay, 2022. https://theses.hal.science/tel-03881392.

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Avec le développement des radiotélescopes de taille continentale, l'analyse des données radio-interférométriques multifréquences devient un enjeu majeur tant en traitement du signal qu'en astrophysique. A cet égard, les méthodes de séparation de composantes constituent un outil adéquat car elles permettent de séparer les données multifréquences en composantes physiques élémentaires.Cependant, les algorithmes standard de séparation de composantes ne sont pas adaptés aux données radio-interférométriques à plus d'un titre : (i) celles-ci sont composées de mesures incomplètes dans le domaine de Fo

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Більше джерел

Книги з теми "Sparsity Problem"

Flemming, Jens. Variational Source Conditions, Quadratic Inverse Problems, Sparsity Promoting Regularization. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95264-2.

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Sparsity Algorithms and Combinatorics. Springer, 2012.

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Flemming, Jens. Variational Source Conditions, Quadratic Inverse Problems, Sparsity Promoting Regularization: New Results in Modern Theory of Inverse Problems and an ... in Laser Optics. Birkhäuser, 2018.

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Newman, Mark. Mathematics of networks. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198805090.003.0006.

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An introduction to the mathematical tools used in the study of networks. Topics discussed include: the adjacency matrix; weighted, directed, acyclic, and bipartite networks; multilayer and dynamic networks; trees; planar networks. Some basic properties of networks are then discussed, including degrees, density and sparsity, paths on networks, component structure, and connectivity and cut sets. The final part of the chapter focuses on the graph Laplacian and its applications to network visualization, graph partitioning, the theory of random walks, and other problems.

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Herbst, Jeffrey. Introduction. Princeton University Press, 2017. http://dx.doi.org/10.23943/princeton/9780691164137.003.0010.

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This introductory chapter explains that the book examines state-building and consolidation in Africa over the last several hundred years by focusing on the fundamental problem confronting leaders of almost all African states: how to broadcast power over sparsely settled lands. The book’s fundamental assumption is that states are only viable if they are able to control the territory defined by their borders. It argues that the failure of many African states to consolidate their authority has resulted in civil wars in some countries, the presence of millions of refugees throughout the continent,

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VECOLI, N. Quaderno Delle Password + Corso Su Come Tenere Al Sicuro le Vostre Password: Per una Persona Che Si Scrive le Password Su Bigliettini Volanti Sparsi per Casa, - Crea Ordine Alfabetico - Lettere Maiuscole per Persone con Problemi Di Vista. Independently Published, 2022.

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Частини книг з теми "Sparsity Problem"

Allison, Ben, David Guthrie, and Louise Guthrie. "Another Look at the Data Sparsity Problem." In Text, Speech and Dialogue. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11846406_41.

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Jain, Avita Fuskele, Santosh Kumar Vishwakarma, and Prashant Jain. "An Efficient Collaborative Recommender System for Removing Sparsity Problem." In ICT Analysis and Applications. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0630-7_14.

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Mishra, Nitin, Saumya Chaturvedi, Vimal Mishra, Rahul Srivastava, and Pratibha Bargah. "Solving Sparsity Problem in Rating-Based Movie Recommendation System." In Advances in Intelligent Systems and Computing. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3874-7_11.

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Papagelis, Manos, Dimitris Plexousakis, and Themistoklis Kutsuras. "Alleviating the Sparsity Problem of Collaborative Filtering Using Trust Inferences." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11429760_16.

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Abdelkhalek, Raoua, Imen Boukhris, and Zied Elouedi. "An Evidential Collaborative Filtering Dealing with Sparsity Problem and Data Imperfections." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-16660-1_51.

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Lee, Sanghack, Jihoon Yang, and Sung-Yong Park. "Discovery of Hidden Similarity on Collaborative Filtering to Overcome Sparsity Problem." In Discovery Science. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30214-8_36.

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De Pessemier, Toon, Kris Vanhecke, Simon Dooms, Tom Deryckere, and Luc Martens. "Extending User Profiles in Collaborative Filtering Algorithms to Alleviate the Sparsity Problem." In Lecture Notes in Business Information Processing. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22810-0_17.

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Nguyen, Van-Doan, and Van-Nam Huynh. "A Community-Based Collaborative Filtering System Dealing with Sparsity Problem and Data Imperfections." In Lecture Notes in Computer Science. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13560-1_74.

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Wen, Hailong, Guiguang Ding, Cong Liu, and Jianming Wang. "Matrix Factorization Meets Cosine Similarity: Addressing Sparsity Problem in Collaborative Filtering Recommender System." In Web Technologies and Applications. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11116-2_27.

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Dixit, Veer Sain, and Parul Jain. "An Improved Similarity Measure to Alleviate Sparsity Problem in Context-Aware Recommender Systems." In Towards Extensible and Adaptable Methods in Computing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2348-5_21.

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Тези доповідей конференцій з теми "Sparsity Problem"

Kurniadi, Felix Indra, and Risma Yulistiani. "Handling Sparsity Problem Using Removal and Imputation in the HUPA-UCM Diabetes Type 1 Dataset." In 2024 5th International Conference on Artificial Intelligence and Data Sciences (AiDAS). IEEE, 2024. http://dx.doi.org/10.1109/aidas63860.2024.10730071.

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Kovacs, Peter. "Sparsity Problem Involving Rational Basis Functions." In 2018 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2018. http://dx.doi.org/10.1109/ssp.2018.8450725.

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Starck, J. L., and M. J. Fadili. "An overview of inverse problem regularization using sparsity." In 2009 16th IEEE International Conference on Image Processing ICIP 2009. IEEE, 2009. http://dx.doi.org/10.1109/icip.2009.5414556.

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Wang, Hao. "DotMat: Solving Cold-Start Problem and Alleviating Sparsity Problem for Recommender Systems." In 2022 IEEE 5th International Conference on Electronics Technology (ICET). IEEE, 2022. http://dx.doi.org/10.1109/icet55676.2022.9824188.

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Montoya-Martinez, Jair, Antonio Artes-Rodriguez, Lars K. Hansen, and Massimiliano Pontil. "Structured sparsity regularization approach to the EEG inverse problem." In 2012 3rd International Workshop on Cognitive Information Processing (CIP). IEEE, 2012. http://dx.doi.org/10.1109/cip.2012.6232898.

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Bolstad, Andrew, Barry Van Veen, and Robert Nowak. "SPACE-TIME SPARSITY REGULARIZATION FOR THE MAGNETOENCEPHALOGRAPHY INVERSE PROBLEM." In 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro. IEEE, 2007. http://dx.doi.org/10.1109/isbi.2007.357019.

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Lee, Ming-yu, Chiung-wei Huang, and Hahn-ming Lee. "Associating Expertized Information to Alleviate Sparsity Problem in Personalization." In 2006 IEEE International Conference on e-Business Engineering (ICEBE'06). IEEE, 2006. http://dx.doi.org/10.1109/icebe.2006.28.

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Zeng, Cong, Jizhong Zhu, Alberto Borghetti, and Carlo Alberto Nucci. "A New Sparsity Method to Large-scale UC Problem." In 2024 IEEE 2nd International Conference on Power Science and Technology (ICPST). IEEE, 2024. http://dx.doi.org/10.1109/icpst61417.2024.10602415.

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Ranjan, Raju, Sumana Gupta, and K. S. Venkatesh. "Sparsity based face modelling and detection with small sample problem." In 2014 Twentieth National Conference on Communications (NCC). IEEE, 2014. http://dx.doi.org/10.1109/ncc.2014.6811268.

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Djuana, Endang, Yue Xu, Yuefeng Li, and Audun Josang. "A Combined Method for Mitigating Sparsity Problem in Tag Recommendation." In 2014 47th Hawaii International Conference on System Sciences (HICSS). IEEE, 2014. http://dx.doi.org/10.1109/hicss.2014.120.

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Звіти організацій з теми "Sparsity Problem"

Singaraju, Dheeraj, Roberto Tron, Ehsan Elhamifar, Allen Yang, and S. S. Sastry. On the Lagrangian Biduality of Sparsity Minimization Problems. Defense Technical Information Center, 2011. http://dx.doi.org/10.21236/ada558990.

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Yu, Guoshen, Guillermo Sapiro, and Stephane Mallat. Solving Inverse Problems with Piecewise Linear Estimators: From Gaussian Mixture Models to Structured Sparsity. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada540722.

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Lewis, Dustin, Gabriella Blum, and Naz Modirzadeh. Indefinite War: Unsettled International Law on the End of Armed Conflict. Harvard Law School Program on International Law and Armed Conflict, 2017. http://dx.doi.org/10.54813/yrjv6070.

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Анотація:

Can we say, definitively, when an armed conflict no longer exists under international law? The short, unsatisfying answer is sometimes: it is clear when some conflicts terminate as a matter of international law, but a decisive determination eludes many others. The lack of fully-settled guidance often matters significantly. That is because international law tolerates, for the most part, far less violent harm, devastation, and suppression in situations other than armed conflicts. Thus, certain measures governed by the laws and customs of war—including killing and capturing the enemy, destroying

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