Relevant bibliographies by topics / L0 regularization

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'L0 regularization'

Author: Grafiati
Published: 25 May 2024
Last updated: 31 July 2025

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Journal articles on the topic "L0 regularization"

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Zhu, Jiehua, and Xiezhang Li. "A Smoothed l0-Norm and l1-Norm Regularization Algorithm for Computed Tomography." Journal of Applied Mathematics 2019 (June 2, 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 resea
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Li, Xiezhang, Guocan Feng, and 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 (August 28, 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 mak
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Fan, Qinwei, and Ting Liu. "Smoothing L0 Regularization for Extreme Learning Machine." Mathematical Problems in Engineering 2020 (July 6, 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 re
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Zhou, Xiaoqing, Rongrong Hou, and Yuhan Wu. "Structural damage detection based on iteratively reweighted l1 regularization algorithm." Advances in Structural Engineering 22, no. 6 (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 comp
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Li, Kun, Na Qi, and Qing Zhu. "Fluid Simulation with an L0 Based Optical Flow Deformation." Applied Sciences 10, no. 18 (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
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Zhang, Lingli, and 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
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Lee, Kyung-Sik. "Signomial Classification Method with0-regularization." IE interfaces 24, no. 2 (2011): 151–55. http://dx.doi.org/10.7232/ieif.2011.24.2.151.

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Frommlet, Florian, and Grégory Nuel. "An Adaptive Ridge Procedure for L0 Regularization." PLOS ONE 11, no. 2 (2016): e0148620. http://dx.doi.org/10.1371/journal.pone.0148620.

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Wang, Guodong. "Image Decomposition Model OSV with L0 Sparse Regularization." Journal of Information and Computational Science 12, no. 2 (2015): 743–50. http://dx.doi.org/10.12733/jics20105230.

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Christou, Antonis, and Andreas Artemiou. "Adaptive L0 Regularization for Sparse Support Vector Regression." Mathematics 11, no. 13 (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 v
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Dissertations / Theses on the topic "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 pé
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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
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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 reg
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Book chapters on the topic "L0 regularization"

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Liu, Weiyou, Zhengyang Li, and Weitong Chen. "Evaluating Model Robustness Using Adaptive Sparse L0 Regularization." In Lecture Notes in Computer Science. Springer Nature Singapore, 2024. https://doi.org/10.1007/978-981-96-0814-0_1.

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

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

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

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Conference papers on the topic "L0 regularization"

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Chen, Jun, Zemin Cai, Xiaohua Xie, and 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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Guo, Yang, Tai Gao, Chengzhi Deng, Shengqian Wang, and JianPing Xiao. "Sparse Unmixing using an approximate L0 Regularization." In First International Conference on Information Sciences, Machinery, Materials and Energy. Atlantis Press, 2015. http://dx.doi.org/10.2991/icismme-15.2015.189.

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Formanek, Andras, and 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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Li, Haoxiang, and 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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Xie, Qixin, Chao Li, Boyu Diao, Zhulin An, and 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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De Boom, Cedric, Samuel Wauthier, Tim Verbelen, and 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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Zhenxing, Liu, and Zeng Xueying. "Mixed impulse and Gaussian noise removal using L0 sparse regularization." In Twelfth International Conference on Graphics and Image Processing, edited by Zhigeng Pan and Xinhong Hei. SPIE, 2021. http://dx.doi.org/10.1117/12.2589376.

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

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Delmer, Alice, Anne Ferreol, and 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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Guo, Kaiwen, Feng Xu, Yangang Wang, Yebin Liu, and 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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