Thematische Bibliographien / L0 regularization

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Auswahl der wissenschaftlichen Literatur zum Thema „L0 regularization“

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Veröffentlicht am 25. Mai 2024

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Zeitschriftenartikel zum Thema "L0 regularization"

1

Zhu, Jiehua, und Xiezhang Li. „A Smoothed l0-Norm and l1-Norm Regularization Algorithm for Computed Tomography“. Journal of Applied Mathematics 2019 (02.06.2019): 1–8. http://dx.doi.org/10.1155/2019/8398035.

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The nonmonotone alternating direction algorithm (NADA) was recently proposed for effectively solving a class of equality-constrained nonsmooth optimization problems and applied to the total variation minimization in image reconstruction, but the reconstructed images suffer from the artifacts. Though by the l0-norm regularization the edge can be effectively retained, the problem is NP hard. The smoothed l0-norm approximates the l0-norm as a limit of smooth convex functions and provides a smooth measure of sparsity in applications. The smoothed l0-norm regularization has been an attractive research topic in sparse image and signal recovery. In this paper, we present a combined smoothed l0-norm and l1-norm regularization algorithm using the NADA for image reconstruction in computed tomography. We resolve the computation challenge resulting from the smoothed l0-norm minimization. The numerical experiments demonstrate that the proposed algorithm improves the quality of the reconstructed images with the same cost of CPU time and reduces the computation time significantly while maintaining the same image quality compared with the l1-norm regularization in absence of the smoothed l0-norm.
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2

Li, Xiezhang, Guocan Feng und Jiehua Zhu. „An Algorithm of l1-Norm and l0-Norm Regularization Algorithm for CT Image Reconstruction from Limited Projection“. International Journal of Biomedical Imaging 2020 (28.08.2020): 1–6. http://dx.doi.org/10.1155/2020/8873865.

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The l1-norm regularization has attracted attention for image reconstruction in computed tomography. The l0-norm of the gradients of an image provides a measure of the sparsity of gradients of the image. In this paper, we present a new combined l1-norm and l0-norm regularization model for image reconstruction from limited projection data in computed tomography. We also propose an algorithm in the algebraic framework to solve the optimization effectively using the nonmonotone alternating direction algorithm with hard thresholding method. Numerical experiments indicate that this new algorithm makes much improvement by involving l0-norm regularization.
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3

Fan, Qinwei, und Ting Liu. „Smoothing L0 Regularization for Extreme Learning Machine“. Mathematical Problems in Engineering 2020 (06.07.2020): 1–10. http://dx.doi.org/10.1155/2020/9175106.

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Extreme learning machine (ELM) has been put forward for single hidden layer feedforward networks. Because of its powerful modeling ability and it needs less human intervention, the ELM algorithm has been used widely in both regression and classification experiments. However, in order to achieve required accuracy, it needs many more hidden nodes than is typically needed by the conventional neural networks. This paper considers a new efficient learning algorithm for ELM with smoothing L0 regularization. A novel algorithm updates weights in the direction along which the overall square error is reduced the most and then this new algorithm can sparse network structure very efficiently. The numerical experiments show that the ELM algorithm with smoothing L0 regularization has less hidden nodes but better generalization performance than original ELM and ELM with L1 regularization algorithms.
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Lee, Kyung-Sik. „Signomial Classification Method with0-regularization“. IE interfaces 24, Nr. 2 (01.06.2011): 151–55. http://dx.doi.org/10.7232/ieif.2011.24.2.151.

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5

Zhou, Xiaoqing, Rongrong Hou und Yuhan Wu. „Structural damage detection based on iteratively reweighted l1 regularization algorithm“. Advances in Structural Engineering 22, Nr. 6 (07.12.2018): 1479–87. http://dx.doi.org/10.1177/1369433218817138.

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Structural damage usually appears in a few sections or members only, which is sparse compared with the total elements of the entire structure. According to the sparse recovery theory, the recently developed damage detection methods employ the l1 regularization technique to exploit the sparsity condition of structural damage. However, in practice, the solution obtained by the l1 regularization is typically suboptimal. The l0 regularization technique outperforms the l1 regularization in various aspects for sparse recovery, whereas the associated nonconvex optimization problem is NP-hard and computationally infeasible. In this study, a damage detection method based on the iteratively reweighted l1 regularization algorithm is proposed. An iterative procedure is employed such that the nonconvex optimization problem of the l0 regularization can be efficiently solved through transforming it into a series of weighted l1 regularization problems. Experimental example demonstrates that the proposed damage detection method can accurately locate the sparse damage over a large number of elements. The advantage of the iteratively reweighted l1 regularization algorithm over the l1 regularization in damage detection is also demonstrated.
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Li, Kun, Na Qi und Qing Zhu. „Fluid Simulation with an L0 Based Optical Flow Deformation“. Applied Sciences 10, Nr. 18 (12.09.2020): 6351. http://dx.doi.org/10.3390/app10186351.

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Fluid simulation can be automatically interpolated by using data-driven fluid simulations based on a space-time deformation. In this paper, we propose a novel data-driven fluid simulation scheme with the L0 based optical flow deformation method by matching two fluid surfaces rather than the L2 regularization. The L0 gradient smooth regularization can result in prominent structure of the fluid in a sparsity-control manner, thus the misalignment of the deformation can be suppressed. We adopt the objective function using an alternating minimization with a half-quadratic splitting for solving the L0 based optical flow deformation model. Experiment results demonstrate that our proposed method can generate more realistic fluid surface with the optimal space-time deformation under the L0 gradient smooth constraint than the L2 one, and outperform the state-of-the-art methods in terms of both objective and subjective quality.
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Frommlet, Florian, und Grégory Nuel. „An Adaptive Ridge Procedure for L0 Regularization“. PLOS ONE 11, Nr. 2 (05.02.2016): e0148620. http://dx.doi.org/10.1371/journal.pone.0148620.

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8

Zhang, Lingli, und An Luo. „l1/2 regularization for wavelet frames based few-view CT reconstruction“. E3S Web of Conferences 269 (2021): 01020. http://dx.doi.org/10.1051/e3sconf/202126901020.

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Reducing the radiation exposure in computed tomography (CT) is always a significant research topic in radiology. Image reconstruction from few-view projection is a reasonable and effective way to decrease the number of rays to lower the radiation exposure. But how to maintain high image reconstruction quality while reducing radiation exposure is a major challenge. To solve this problem, several researchers are absorbed in l0 or l1 regularization based optimization models to deal with it. However, the solution of l1 regularization based optimization model is not sparser than that of l1/2 or l0 regularization, and solving the l0 regularization is more difficult than solving the l1/2 regularization. In this paper, we develop l1/2 regularization for wavelet frames based image reconstruction model to research the few-view problem. First, the existence of the solution of the corresponding model is demonstrated. Second, an alternate direction method (ADM) is utilized to separate the original problem into two subproblems, where the former subproblem about the image is solved using the idea of the proximal mapping, the simultaneous algebraic reconstruction technique (SART) and the projection and contraction (PC) algorithm, and the later subproblem about the wavelet coefficients is solved using the half thresholding (HT) algorithm. Furthermore, the convergence analysis of our method is given by the simulated implementions. Simulated and real experiments confirm the effectiveness of our method.
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Wang, Guodong. „Image Decomposition Model OSV with L0 Sparse Regularization“. Journal of Information and Computational Science 12, Nr. 2 (20.01.2015): 743–50. http://dx.doi.org/10.12733/jics20105230.

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10

Christou, Antonis, und Andreas Artemiou. „Adaptive L0 Regularization for Sparse Support Vector Regression“. Mathematics 11, Nr. 13 (22.06.2023): 2808. http://dx.doi.org/10.3390/math11132808.

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In this work, we proposed a sparse version of the Support Vector Regression (SVR) algorithm that uses regularization to achieve sparsity in function estimation. To achieve this, we used an adaptive L0 penalty that has a ridge structure and, therefore, does not introduce additional computational complexity to the algorithm. In addition to this, we used an alternative approach based on a similar proposal in the Support Vector Machine (SVM) literature. Through numerical studies, we demonstrated the effectiveness of our proposals. We believe that this is the first time someone discussed a sparse version of Support Vector Regression (in terms of variable selection and not in terms of support vector selection).
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Dissertationen zum Thema "L0 regularization"

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Bechensteen, Arne. „Optimisation L2-L0 contrainte et application à la microscopie à molécule unique“. Thesis, Université Côte d'Azur, 2020. http://www.theses.fr/2020COAZ4068.

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L'optimisation parcimonieuse est cruciale dans la société d'aujourd'hui, car elle est utilisée dans de nombreux domaines, tels que le débruitage, la compression, l’apprentissage et la sélection de caractéristiques. Cependant, obtenir une bonne solution parcimonieuse d'un signal est un défi de calcul.Cette thèse se concentre sur l'optimisation d’un terme des moindres carrés en norme L0 sous une contrainte de k-parcimonie sur la solution exprimée avec la pseudo-norme L0 (le problème L2-L0 contraint). Nous étudions également la somme de la fonction de perte des moindres carrés et d'un terme de pénalité L0 (le problème L2-L0 pénalisé). Les deux problèmes sont non convexes, non continus et NP-durs. Nous proposons trois nouvelles approches d'optimisation parcimonieuse. Nous présentons d'abord une relaxation continue du problème contraint et présentons une méthode pour minimiser la relaxation proposée. Deuxièmement, nous reformulons la pseudo-norme L0 comme un problème de minimisation convexe. Ceci est fait en introduisant une variable auxiliaire, et nous présentons une reformulation exacte du problème contraint (CoBic) et pénalisé (PeBic). Enfin, nous présentons une méthode pour minimiser le produit du terme de fidélité des données et du terme de régularisation. Ce dernier est un travail de recherche en cours. Nous appliquons les trois premières méthodes proposées (relaxation, CoBic et PeBic) à la microscopie par molécule unique. Les résultats des algorithmes proposés sont à l'état de l'art des méthodes basées sur la grille. De plus, fixer la constante de contrainte de parcimonie est généralement plus intuitif que fixer le paramètre de pénalité, ce qui rend l’approche contrainte attractive pour les applications
Sparse optimization is crucial in today's society, as this is used in multiple domains, such as denoising, compression, machine learning, and variable selection. Sparse optimization is also vital in single-molecule localization microscopy, a microscopy method widely used in biology. However, obtaining a good sparse solution of a signal is computationally challenging. This thesis focuses on sparse optimization in the form of minimizing the least square loss function under a k-sparse constraint with an L0 pseudo-norm (the constrained L2-L0 problem). We also study the sum of the least square loss function and an L0 penalty term (the penalized L2-L0 problem). Both problems are non-convex, non-continuous, and NP-hard. We propose three new approaches to sparse optimization. We present first a continuous relaxation of the constrained problem and present a method to minimize the proposed relaxation. Secondly, we reformulate the L0 pseudo-norm as a convex minimization problem. This is done by introducing an auxiliary variable, and we present an exact biconvex reformulation of the constrained (CoBic) and penalized (PeBic) problems. Finally, we present a method to minimize the product of the data fidelity term and the regularization term. The latter is still an ongoing research work. We apply the three proposed methods (relaxation, CoBic, and PeBic) to single-molecule localization microscopy and compare them with other commonly used algorithms in sparse optimization. The proposed algorithms' results are as good as the state-of-the-art in grid-based methods. Furthermore, fixing the sparsity constraint constant is usually more intuitive than fixing the penalty parameter, making the constraint approach attractive for applications
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Delmer, Alice. „Goniométrie parcimonieuse de sources radioélectriques : modèles, algorithmes et mises en œuvre robustes“. Electronic Thesis or Diss., université Paris-Saclay, 2021. http://www.theses.fr/2021UPASG085.

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Cette thèse porte sur la goniométrie d'émetteurs radioélectriques non coopératifs à partir de signaux reçus sur un réseau d'antennes. Les applications visées dans ce travail sont le scénario aéroporté, caractérisé par un nombre de source supérieur au nombre de capteurs, et le scénario en environnement urbain, caractérisé par des multi-trajets cohérents.Les méthodes de goniométrie conventionnelles telles que formation de voies et Capon ou encore les méthodes à haute résolution telles que MUSIC ne sont pas performantes dans de tels scénarios. La méthode du maximum de vraisemblance souffre d'une complexité de calcul incompatible avec les systèmes opérationnels actuels.Pour palier ces limitations, la goniométrie est traitée ici avec un formalisme parcimonieux, parfaitement adapté à l'utilisation de tables de calibration dans les systèmes opérationnels. Après avoir montré les intérêts d'une approche reposant sur une régularisation par la norme L0, cette thèse s'attaque aux verrous que sont le paramètre de régularisation et la convergence globale des algorithmes d'optimisation. Pour cela, nous construisons et étudions statistiquement des représentations parcimonieuses adaptées i) aux scénarios aéroportés, et ii) aux environnements urbains. L'équivalence avec le maximum de vraisemblance donné par une formulation contrainte permet alors de déterminer un intervalle théorique admissible pour le paramètre de régularisation. Nous étudions également les minimiseurs et les surfaces d’erreurs de différents critères d’optimisation. Cela nous permet de proposer des schémas itératifs de minimisation augmentant la probabilité de convergence globale et ainsi moins sensibles à l’initialisation. L'algorithme ALICE-L0 ainsi proposé permet de plus de séparer des sources proches
This thesis deals with the direction-of-arrival (DOA) estimation of non-cooperative radio transmitters from signals received on an antenna array. The applications targeted in this work are the airborne scenario, characterized by a number of sources higher than the number of sensors, and the urban environment scenario, characterized by coherent multipath.Conventional direction-of-arrival estimation methods such as beamforming and Capon method or high resolution methods such as MUSIC are not efficient in such scenarios. The maximum likelihood method suffers from a computational complexity incompatible with current operational systems.In order to overcome these limitations, the problem of direction-of-arrival estimation is treated here with a sparse formalism, perfectly adapted to the use of calibration tables in operational systems. After having shown the interests of an approach based on a regularization by the L0 norm, this thesis tackles the technical issues that are the regularization parameter and the global convergence of optimization algorithms. To this end, we construct and statistically study sparse representations adapted to i) airborne scenarios, and ii) urban environments. The equivalence with the maximum likelihood given by a constrained formulation then allows us to determine a theoretical admissible interval for the regularization parameter. We also study the minimizers and error surfaces of different optimization criteria. This allows us to propose iterative minimization schemes that increase the probability of global convergence and thus are less sensitive to initialization. In that respect, the proposed ALICE-L0 algorithm enables to separate close sources
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Arceci, Francesca. „Variational algorithms for image Super Resolution“. Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/19509/.

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La Super Resolution è una tecnica che permette di aumentare la risoluzione di un’immagine oltre i limiti imposti dai sensori. Nel processo di acquisizione e formazione dell’immagine, vi sono infatti fenomeni di noise e blurring che la corrompono: da qui l’esigenza di ricostruire l’input reale. Una volta modellizzato questo processo, vi sono svariate tecniche SR che approcciano in modi differenti al problema: in questo lavoro ci basiamo su teniche reconstruction-based che prevedono la minimizzazione di due funzionali, uno che misura la coerenza tra dato e soluzione, l’altro è un termine di regolarizzazione. Lo studio di questa tesi si basa su un’immagine con gradiente sparso, più precisamente un QR code: partendo dalla descrizione del modello matematico, il nostro lavoro sarà quello di trovare un funzionale di regolarizzazione che esprima la proprietà del gradiente sparso e, basandoci sull’approccio dell’ Alternating Direction Method of Multipliers, implementare un nuovo algoritmo che risolva il problema di minimo ad esso associato. Mostreremo i risultati ottenuti confrontandoli con algoritmi preesistenti per provarne la buona performance.
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Buchteile zum Thema "L0 regularization"

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Wang, Liansheng, Xinyue Li, Yiping Chen und Jing Qin. „Application of L0-Norm Regularization to Epicardial Potential Reconstruction“. In Lecture Notes in Computer Science, 493–500. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24571-3_59.

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Li, Li, Fangwan Huang und Zhiyong Yu. „Echo State Network Based on L0 Norm Regularization for Chaotic Time Series Prediction“. In Green, Pervasive, and Cloud Computing, 145–52. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64243-3_12.

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3

Shi, Mingzhu. „A Novel Gradient L0-Norm Regularization Image Restoration Method Based on Non-local Total Variation“. In Lecture Notes in Electrical Engineering, 487–93. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-13-9409-6_57.

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Konferenzberichte zum Thema "L0 regularization"

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Chen, Jun, Zemin Cai, Xiaohua Xie und Jianhuang Lai. „Motion Estimation with L0 Norm Regularization“. In 2021 IEEE 7th International Conference on Virtual Reality (ICVR). IEEE, 2021. http://dx.doi.org/10.1109/icvr51878.2021.9483834.

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2

Guo, Yang, Tai Gao, Chengzhi Deng, Shengqian Wang und JianPing Xiao. „Sparse Unmixing using an approximate L0 Regularization“. In First International Conference on Information Sciences, Machinery, Materials and Energy. Paris, France: Atlantis Press, 2015. http://dx.doi.org/10.2991/icismme-15.2015.189.

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3

Formanek, Andras, und Daniel Hadhazi. „Compressing Convolutional Neural Networks by L0 Regularization“. In 2019 International Conference on Control, Artificial Intelligence, Robotics & Optimization (ICCAIRO). IEEE, 2019. http://dx.doi.org/10.1109/iccairo47923.2019.00032.

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4

Li, Haoxiang, und Jianmin Zheng. „L0-Regularization based Material Design for Hexahedral Mesh Models“. In CAD'21. CAD Solutions LLC, 2021. http://dx.doi.org/10.14733/cadconfp.2021.314-318.

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5

Xie, Qixin, Chao Li, Boyu Diao, Zhulin An und Yongjun Xu. „L0 Regularization based Fine-grained Neural Network Pruning Method“. In 2019 11th International Conference on Electronics, Computers and Artificial Intelligence (ECAI). IEEE, 2019. http://dx.doi.org/10.1109/ecai46879.2019.9041962.

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6

De Boom, Cedric, Samuel Wauthier, Tim Verbelen und Bart Dhoedt. „Dynamic Narrowing of VAE Bottlenecks Using GECO and L0 Regularization“. In 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021. http://dx.doi.org/10.1109/ijcnn52387.2021.9533671.

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7

Zhenxing, Liu, und Zeng Xueying. „Mixed impulse and Gaussian noise removal using L0 sparse regularization“. In Twelfth International Conference on Graphics and Image Processing, herausgegeben von Zhigeng Pan und Xinhong Hei. SPIE, 2021. http://dx.doi.org/10.1117/12.2589376.

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Han, Xiaoyu, Yannan Yang und Wende Dong. „Image denoising based on hybrid L0 and L1-norm regularization“. In Novel Imaging System, herausgegeben von Bo Liu, Yan Zhou, Qiang Zhang und Feihu Xu. SPIE, 2024. http://dx.doi.org/10.1117/12.3015106.

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9

Delmer, Alice, Anne Ferreol und Pascal Larzabal. „On Regularization Parameter for L0-Sparse Covariance Fitting Based DOA Estimation“. In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9053963.

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10

Guo, Kaiwen, Feng Xu, Yangang Wang, Yebin Liu und Qionghai Dai. „Robust Non-rigid Motion Tracking and Surface Reconstruction Using L0 Regularization“. In 2015 IEEE International Conference on Computer Vision (ICCV). IEEE, 2015. http://dx.doi.org/10.1109/iccv.2015.353.

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