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Zeitschriftenartikel zum Thema „L0 regularization“

Autor: Grafiati

Veröffentlicht am 25. Mai 2024

Zuletzt aktualisiert am 19. Juli 2025

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1

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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3

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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5

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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7

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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8

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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9

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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10

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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11

Wang, Yangyang, Zhiming He, Xu Zhan, Yuanhua Fu, and Liming Zhou. "Three-Dimensional Sparse SAR Imaging with Generalized Lq Regularization." Remote Sensing 14, no. 2 (2022): 288. http://dx.doi.org/10.3390/rs14020288.

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Three-dimensional (3D) synthetic aperture radar (SAR) imaging provides complete 3D spatial information, which has been used in environmental monitoring in recent years. Compared with matched filtering (MF) algorithms, the regularization technique can improve image quality. However, due to the substantial computational cost, the existing observation-matrix-based sparse imaging algorithm is difficult to apply to large-scene and 3D reconstructions. Therefore, in this paper, novel 3D sparse reconstruction algorithms with generalized Lq-regularization are proposed. First, we combine majorization–mi

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12

Xiang, Jianhong, Huihui Yue, Xiangjun Yin, and Guoqing Ruan. "A Reweighted Symmetric Smoothed Function Approximating L0-Norm Regularized Sparse Reconstruction Method." Symmetry 10, no. 11 (2018): 583. http://dx.doi.org/10.3390/sym10110583.

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Sparse-signal recovery in noisy conditions is a problem that can be solved with current compressive-sensing (CS) technology. Although current algorithms based on L 1 regularization can solve this problem, the L 1 regularization mechanism cannot promote signal sparsity under noisy conditions, resulting in low recovery accuracy. Based on this, we propose a regularized reweighted composite trigonometric smoothed L 0 -norm minimization (RRCTSL0) algorithm in this paper. The main contributions of this paper are as follows: (1) a new smoothed symmetric composite trigonometric (CT) function is propos

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Li, Haoxiang, and Jianmin Zheng. "L0-Regularization based Material Design for Hexahedral Mesh Models." Computer-Aided Design and Applications 19, no. 6 (2022): 1171–83. http://dx.doi.org/10.14733/cadaps.2022.1171-1183.

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14

Yan Jingwen, 闫敬文, 谢婷婷 Xie Tingting, 彭鸿 Peng Hong, and 刘攀华 Liu Panhua. "Motion Image Deblurring Based on L0 Norms Regularization Term." Laser & Optoelectronics Progress 54, no. 2 (2017): 021005. http://dx.doi.org/10.3788/lop54.021005.

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15

Zhao, Yong, Hong Qin, Xueying Zeng, Junli Xu, and Junyu Dong. "Robust and effective mesh denoising using L0 sparse regularization." Computer-Aided Design 101 (August 2018): 82–97. http://dx.doi.org/10.1016/j.cad.2018.04.001.

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16

Dai, Ronghuo, and Jun Yang. "Amplitude-Versus-Angle (AVA) Inversion for Pre-Stack Seismic Data with L0-Norm-Gradient Regularization." Mathematics 11, no. 4 (2023): 880. http://dx.doi.org/10.3390/math11040880.

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Amplitude-versus-angle (AVA) inversion for pre-stack seismic data is a key technology in oil and gas reservoir prediction. Conventional AVA inversion contains two main stages. Stage one estimates the relative change rates of P-wave velocity, S-wave velocity and density, and stage two obtains the P-wave velocity, S-wave velocity and density based on their relative change rates through trace integration. An alternative way merges these two stages to estimate P-wave velocity, S-wave velocity and density directly. This way is less sensitive to noise in seismic data compared to conventional two-sta

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Lee, Han-Sol, Changgyun Jin, Chanwoo Shin, and Seong-Eun Kim. "Sparse Diffusion Least Mean-Square Algorithm with Hard Thresholding over Networks." Mathematics 11, no. 22 (2023): 4638. http://dx.doi.org/10.3390/math11224638.

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This paper proposes a distributed estimation technique utilizing the diffusion least mean-square (LMS) algorithm, specifically designed for sparse systems in which many coefficients of the system are zeros. To efficiently utilize the sparse representation of the system and achieve a promising performance, we have incorporated L0-norm regularization into the diffusion LMS algorithm. This integration is accomplished by employing hard thresholding through a variable splitting method into the update equation. The efficacy of our approach is validated by comprehensive theoretical analysis, rigorous

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Yang, Shuifeng, Yong Zhao, Xingyu Tuo, Deqing Mao, Yin Zhang, and Jianyu Yang. "Real Aperture Radar Angular Super-Resolution Imaging Using Modified Smoothed L0 Norm with a Regularization Strategy." Remote Sensing 16, no. 1 (2023): 12. http://dx.doi.org/10.3390/rs16010012.

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Restricted by the ill-posed antenna measurement matrix, the conventional smoothed L0 norm algorithm (SL0) fails to enable direct real aperture radar angular super-resolution imaging. This paper proposes a modified smoothed L0 norm (MSL0) algorithm to address this issue. First, as the pseudo-inverse of the ill-posed antenna measurement matrix is required to set the initial values and calculate the gradient projection, a regularization strategy is employed to relax the ill-posedness. Based on the regularization strategy, the proposed MSL0 algorithm can avoid noise amplification when faced with t

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Cao, Chen, Matthew Greenberg, and Quan Long. "WgLink: reconstructing whole-genome viral haplotypes using L0+L1-regularization." Bioinformatics 37, no. 17 (2021): 2744–46. http://dx.doi.org/10.1093/bioinformatics/btab076.

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Abstract Summary Many tools can reconstruct viral sequences based on next-generation sequencing reads. Although existing tools effectively recover local regions, their accuracy suffers when reconstructing the whole viral genomes (strains). Moreover, they consume significant memory when the sequencing coverage is high or when the genome size is large. We present WgLink to meet this challenge. WgLink takes local reconstructions produced by other tools as input and patches the resulting segments together into coherent whole-genome strains. We accomplish this using an L0+L1-regularized regression,

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Xiang, Jianhong, Huihui Yue, Xiangjun Yin, and Linyu Wang. "A New Smoothed L0 Regularization Approach for Sparse Signal Recovery." Mathematical Problems in Engineering 2019 (July 17, 2019): 1–12. http://dx.doi.org/10.1155/2019/1978154.

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Sparse signal reconstruction, as the main link of compressive sensing (CS) theory, has attracted extensive attention in recent years. The essence of sparse signal reconstruction is how to recover the original signal accurately and effectively from an underdetermined linear system equation (ULSE). For this problem, we propose a new algorithm called regularization reweighted smoothed L0 norm minimization algorithm, which is simply called RRSL0 algorithm. Three innovations are made under the framework of this method: (1) a new smoothed function called compound inverse proportional function (CIPF)

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Zhu, Jun, Changwei Chen, Shoubao Su, and Zinan Chang. "Compressive Sensing of Multichannel EEG Signals via lq Norm and Schatten-p Norm Regularization." Mathematical Problems in Engineering 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/2189563.

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In Wireless Body Area Networks (WBAN) the energy consumption is dominated by sensing and communication. Recently, a simultaneous cosparsity and low-rank (SCLR) optimization model has shown the state-of-the-art performance in compressive sensing (CS) recovery of multichannel EEG signals. How to solve the resulting regularization problem, involving l0 norm and rank function which is known as an NP-hard problem, is critical to the recovery results. SCLR takes use of l1 norm and nuclear norm as a convex surrogate function for l0 norm and rank function. However, l1 norm and nuclear norm cannot well

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Zhang, Chuncheng, and Zhiying Long. "Euler’s Elastica Regularization for Voxel Selection of fMRI Data." International Journal of Signal Processing Systems 8, no. 2 (2020): 32–41. http://dx.doi.org/10.18178/ijsps.8.2.32-41.

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Multivariate analysis methods have been widely applied to functional Magnetic Resonance Imaging (fMRI) data to reveal brain activity patterns and decode brain states. Among the various multivariate analysis methods, the multivariate regression models that take high-dimensional fMRI data as inputs while using relevant regularization were proposed for voxel selection or decoding. Although some previous studies added the sparse regularization to the multivariate regression model to select relevant voxels, the selected sparse voxels cannot be used to map brain activity of each task. Compared to th

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23

Kim, Kyuseok, and Ji-Youn Kim. "Blind Deconvolution Based on Compressed Sensing with bi-l0-l2-norm Regularization in Light Microscopy Image." International Journal of Environmental Research and Public Health 18, no. 4 (2021): 1789. http://dx.doi.org/10.3390/ijerph18041789.

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Blind deconvolution of light microscopy images could improve the ability of distinguishing cell-level substances. In this study, we investigated the blind deconvolution framework for a light microscope image, which combines the benefits of bi-l0-l2-norm regularization with compressed sensing and conjugated gradient algorithms. Several existing regularization approaches were limited by staircase artifacts (or cartooned artifacts) and noise amplification. Thus, we implemented our strategy to overcome these problems using the bi-l0-l2-norm regularization proposed. It was investigated through simu

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Wei, Zhe, Qingfa Li, Jiazhen Wei, and Wei Bian. "Neural network for a class of sparse optimization with L0-regularization." Neural Networks 151 (July 2022): 211–21. http://dx.doi.org/10.1016/j.neunet.2022.03.033.

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Zhang, Fengjun, Wei Lu, Hongmei Liu, and Fei Xue. "Natural image deblurring based on L0-regularization and kernel shape optimization." Multimedia Tools and Applications 77, no. 20 (2018): 26239–57. http://dx.doi.org/10.1007/s11042-018-5847-2.

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Guo, Di, Zhangren Tu, Jiechao Wang, Min Xiao, Xiaofeng Du, and Xiaobo Qu. "Salt and Pepper Noise Removal with Multi-Class Dictionary Learning and L0 Norm Regularizations." Algorithms 12, no. 1 (2018): 7. http://dx.doi.org/10.3390/a12010007.

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Images may be corrupted by salt and pepper impulse noise during image acquisitions or transmissions. Although promising denoising performances have been recently obtained with sparse representations, how to restore high-quality images remains challenging and open. In this work, image sparsity is enhanced with a fast multiclass dictionary learning, and then both the sparsity regularization and robust data fidelity are formulated as minimizations of L0-L0 norms for salt and pepper impulse noise removal. Additionally, a numerical algorithm of modified alternating direction minimization is derived

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Xu, Jian, Lanlan Rao, Franz Schreier, Dmitry S. Efremenko, Adrian Doicu, and Thomas Trautmann. "Insight into Construction of Tikhonov-Type Regularization for Atmospheric Retrievals." Atmosphere 11, no. 10 (2020): 1052. http://dx.doi.org/10.3390/atmos11101052.

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In atmospheric science we are confronted with inverse problems arising in applications associated with retrievals of geophysical parameters. A nonlinear mapping from geophysical quantities (e.g., atmospheric properties) to spectral measurements can be represented by a forward model. An inversion often suffers from the lack of stability and its stabilization introduced by proper approaches, however, can be treated with sufficient generality. In principle, regularization can enforce uniqueness of the solution when additional information is incorporated into the inversion process. In this paper,

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Quasdane, Mohamed, Hassan Ramchoun, and Tawfik Masrour. "Sparse smooth group L0∘L1/2 regularization method for convolutional neural networks." Knowledge-Based Systems 284 (January 2024): 111327. http://dx.doi.org/10.1016/j.knosys.2023.111327.

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29

Feng, Yue, Ronghuo Dai, and Zidan Fan. "Enhanced Small Reflections Sparse-Spike Seismic Inversion with Iterative Hybrid Thresholding Algorithm." Mathematics 13, no. 1 (2024): 37. https://doi.org/10.3390/math13010037.

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Seismic inversion is a process of imaging or predicting the spatial and physical properties of underground strata. The most commonly used one is sparse-spike seismic inversion with sparse regularization. There are many effective methods to solve sparse regularization, such as L0-norm, L1-norm, weighted L1-norm, etc. This paper studies the sparse-spike inversion with L0-norm. It is usually solved by the iterative hard thresholding algorithm (IHTA) or its faster variants. However, hard thresholding algorithms often lead to a sharp increase or numerical oscillation of the residual, which will aff

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Wang, Linyu, Xiangjun Yin, Huihui Yue, and Jianhong Xiang. "A Regularized Weighted Smoothed L0 Norm Minimization Method for Underdetermined Blind Source Separation." Sensors 18, no. 12 (2018): 4260. http://dx.doi.org/10.3390/s18124260.

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Compressed sensing (CS) theory has attracted widespread attention in recent years and has been widely used in signal and image processing, such as underdetermined blind source separation (UBSS), magnetic resonance imaging (MRI), etc. As the main link of CS, the goal of sparse signal reconstruction is how to recover accurately and effectively the original signal from an underdetermined linear system of equations (ULSE). For this problem, we propose a new algorithm called the weighted regularized smoothed L 0 -norm minimization algorithm (WReSL0). Under the framework of this algorithm, we have d

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31

WANG, YANFEI, CHANGCHUN YANG, and JINGJIE CAO. "ON TIKHONOV REGULARIZATION AND COMPRESSIVE SENSING FOR SEISMIC SIGNAL PROCESSING." Mathematical Models and Methods in Applied Sciences 22, no. 02 (2012): 1150008. http://dx.doi.org/10.1142/s0218202511500084.

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Using compressive sensing and sparse regularization, one can nearly completely reconstruct the input (sparse) signal using limited numbers of observations. At the same time, the reconstruction methods by compressing sensing and optimizing techniques overcome the obstacle of the number of sampling requirement of the Shannon/Nyquist sampling theorem. It is well known that seismic reflection signal may be sparse, sometimes and the number of sampling is insufficient for seismic surveys. So, the seismic signal reconstruction problem is ill-posed. Considering the ill-posed nature and the sparsity of

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Guo, Kaiwen, Feng Xu, Yangang Wang, Yebin Liu, and Qionghai Dai. "Errata to “Robust Non-Rigid Motion Tracking and Surface Reconstruction Using L0 Regularization”." IEEE Transactions on Visualization and Computer Graphics 24, no. 7 (2018): 2268. http://dx.doi.org/10.1109/tvcg.2018.2826859.

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Liu, Jingjing, Guoxi Ni, and Shaowen Yan. "Alternating method based on framelet l0-norm and TV regularization for image restoration." Inverse Problems in Science and Engineering 27, no. 6 (2018): 790–807. http://dx.doi.org/10.1080/17415977.2018.1500569.

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Zhang, Yong, Bowei Shen, Shaofan Wang, Dehui Kong, and Baocai Yin. "L0-regularization-based skeleton optimization from consecutive point sets of kinetic human body." ISPRS Journal of Photogrammetry and Remote Sensing 143 (September 2018): 124–33. http://dx.doi.org/10.1016/j.isprsjprs.2018.04.016.

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35

Bouaziz, Olivier, and Grégory Nuel. "L0 Regularization for the Estimation of Piecewise Constant Hazard Rates in Survival Analysis." Applied Mathematics 08, no. 03 (2017): 377–94. http://dx.doi.org/10.4236/am.2017.83031.

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Wang, Bin, Li Wang, Hao Yu, and Fengming Xin. "A New Regularized Reconstruction Algorithm Based on Compressed Sensing for the Sparse Underdetermined Problem and Applications of One-Dimensional and Two-Dimensional Signal Recovery." Algorithms 12, no. 7 (2019): 126. http://dx.doi.org/10.3390/a12070126.

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The compressed sensing theory has been widely used in solving undetermined equations in various fields and has made remarkable achievements. The regularized smooth L0 (ReSL0) reconstruction algorithm adds an error regularization term to the smooth L0(SL0) algorithm, achieving the reconstruction of the signal well in the presence of noise. However, the ReSL0 reconstruction algorithm still has some flaws. It still chooses the original optimization method of SL0 and the Gauss approximation function, but this method has the problem of a sawtooth effect in the later optimization stage, and the conv

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Huang, Kaizhu, Danian Zheng, Irwin King, and Michael R. Lyu. "Arbitrary Norm Support Vector Machines." Neural Computation 21, no. 2 (2009): 560–82. http://dx.doi.org/10.1162/neco.2008.12-07-667.

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Support vector machines (SVM) are state-of-the-art classifiers. Typically L2-norm or L1-norm is adopted as a regularization term in SVMs, while other norm-based SVMs, for example, the L0-norm SVM or even the L∞-norm SVM, are rarely seen in the literature. The major reason is that L0-norm describes a discontinuous and nonconvex term, leading to a combinatorially NP-hard optimization problem. In this letter, motivated by Bayesian learning, we propose a novel framework that can implement arbitrary norm-based SVMs in polynomial time. One significant feature of this framework is that only a sequenc

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Feng, Yayuan, Yu Shi, and Dianjun Sun. "Blind Poissonian Image Deblurring Regularized by a Denoiser Constraint and Deep Image Prior." Mathematical Problems in Engineering 2020 (August 24, 2020): 1–15. http://dx.doi.org/10.1155/2020/9483521.

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The denoising and deblurring of Poisson images are opposite inverse problems. Single image deblurring methods are sensitive to image noise. A single noise filter can effectively remove noise in advance, but it also damages blurred information. To simultaneously solve the denoising and deblurring of Poissonian images better, we learn the implicit deep image prior from a single degraded image and use the denoiser as a regularization term to constrain the latent clear image. Combined with the explicit L0 regularization prior of the image, the denoising and deblurring model of the Poisson image is

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Wang, Chengxiang, Xiaoyan Wang, Kequan Zhao, Min Huang, Xianyun Li, and Wei Yu. "A cascading l0 regularization reconstruction method in nonsubsampled contourlet domain for limited-angle CT." Applied Mathematics and Computation 451 (August 2023): 128013. http://dx.doi.org/10.1016/j.amc.2023.128013.

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40

Heydari, Esmail, Mohammad Shams Esfand Abadi, and Seyed Mahmoud Khademiyan. "Improved multiband structured subband adaptive filter algorithm with L0-norm regularization for sparse system identification." Digital Signal Processing 122 (April 2022): 103348. http://dx.doi.org/10.1016/j.dsp.2021.103348.

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Chen, Tao, and Guibin Zhang. "Mineral Exploration Potential Estimation Using 3D Inversion: A Comparison of Three Different Norms." Remote Sensing 14, no. 11 (2022): 2537. http://dx.doi.org/10.3390/rs14112537.

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Gravity data have been frequently used in researching the subsurface to map the 3D geometry of the density structure, which is considered the basis for further interpretations, such as the estimation of exploration potential in mineral exploration. The gravity inversion, practically employed to map the density structure, can be achieved by different methods. The method based on Tikhonov regularization is the most commonly used among them. Usually, the subsurface is discretized into a set of cells or voxels. To recover a stable and reliable solution, constraints are introduced into the Tikhonov

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Xia Chengquan, 夏成权, 梁建娟 Liang Jianjuan, 刘洪 Liu Hong та 刘本永 Liu Benyong. "联合双通道对比度和L0正则化强度及梯度先验的模糊图像盲复原". Laser & Optoelectronics Progress 59, № 8 (2022): 0811010. http://dx.doi.org/10.3788/lop202259.0811010.

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Shah, Ankur, and Gaurang Vesmawala. "Vibration-based damage detection of beams using Hybrid Genetic Algorithm with combined l0 and l1 regularization." Structures 67 (September 2024): 107006. http://dx.doi.org/10.1016/j.istruc.2024.107006.

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Li, Haorui, Ying Gao, Xinyu Guo, and Shifeng Ou. "Variable-Step-Size Generalized Maximum Correntropy Affine Projection Algorithm with Sparse Regularization Term." Electronics 14, no. 2 (2025): 291. https://doi.org/10.3390/electronics14020291.

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Adaptive filtering plays a pivotal role in modern electronic information and communication systems, particularly in dynamic and complex environments. While traditional adaptive algorithms work well in many scenarios, they do not fully exploit the sparsity of the system, which restricts their performance in the presence of varying noise conditions. To overcome these limitations, this paper proposes a variable-step-size generalized maximum correntropy affine projection algorithm (C-APGMC) with a sparse regularization term. The algorithm leverages the system’s sparsity by using a correlated entro

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Chen, Yan, and Yulu Zhao. "Efficient sparse estimation on interval-censored data with approximated L0 norm: Application to child mortality." PLOS ONE 16, no. 4 (2021): e0249359. http://dx.doi.org/10.1371/journal.pone.0249359.

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A novel penalty for the proportional hazards model under the interval-censored failure time data structure is discussed, with which the subject of variable selection is rarely studied. The penalty comes from an idea to approximate some information criterion, e.g., the BIC or AIC, and the core process is to smooth the ℓ0 norm. Compared with usual regularization methods, the proposed approach is free of heavily time-consuming hyperparameter tuning. The efficiency is further improved by fitting the model and selecting variables in one step. To achieve this, sieve likelihood is introduced, which s

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Tao, Xiangxing, Mingxin Wang, and Yanting Ji. "The Application of Graph-Structured Cox Model in Financial Risk Early Warning of Companies." Sustainability 15, no. 14 (2023): 10802. http://dx.doi.org/10.3390/su151410802.

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An effective financial risk forecast depends on the selection of important indicators from a broad set of financial indicators that are often correlated with one another. In this paper, we address this challenge by proposing a Cox model with a graph structure that allows us to identify and filter out the crucial indicators for financial risk forecasting. The Cox model can be converted to a weighted least squares form for the purpose of solution, where the regularization l0 compresses the signs of the variable coefficients and reduces the error caused by the compression of the coefficients. The

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Gebre, Mesay Geletu, and Elias Lewi. "Gravity inversion method using L₀-norm constraint with auto-adaptive regularization and combined stopping criteria." Solid Earth 14, no. 2 (2023): 101–17. http://dx.doi.org/10.5194/se-14-101-2023.

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Abstract. We present a gravity inversion method that can produce compact and sharp images to assist the modeling of non-smooth geologic features. The proposed iterative inversion approach makes use of L0-norm-stabilizing functional, hard and physical parameter inequality constraints and a depth-weighting function. The method incorporates an auto-adaptive regularization technique, which automatically determines a suitable regularization parameter and error-weighting function that helps to improve both the stability and convergence of the method. The auto-adaptive regularization and error-weight

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Shao, Wenze, Haisong Deng, and Zhuihui Wei. "Nonconvex Compressed Sampling of Natural Images and Applications to Compressed MR Imaging." ISRN Computational Mathematics 2012 (November 16, 2012): 1–12. http://dx.doi.org/10.5402/2012/982792.

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There have been proposed several compressed imaging reconstruction algorithms for natural and MR images. In essence, however, most of them aim at the good reconstruction of edges in the images. In this paper, a nonconvex compressed sampling approach is proposed for structure-preserving image reconstruction, through imposing sparseness regularization on strong edges and also oscillating textures in images. The proposed approach can yield high-quality reconstruction as images are sampled at sampling ratios far below the Nyquist rate, due to the exploitation of a kind of approximate l0 seminorms.

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Su, Yixin, Rui Zhang, Sarah Erfani, and Zhenghua Xu. "Detecting Beneficial Feature Interactions for Recommender Systems." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 5 (2021): 4357–65. http://dx.doi.org/10.1609/aaai.v35i5.16561.

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Feature interactions are essential for achieving high accuracy in recommender systems. Many studies take into account the interaction between every pair of features. However, this is suboptimal because some feature interactions may not be that relevant to the recommendation result and taking them into account may introduce noise and decrease recommendation accuracy. To make the best out of feature interactions, we propose a graph neural network approach to effectively model them, together with a novel technique to automatically detect those feature interactions that are beneficial in terms of

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Zhang, Lingli, Li Zeng, and Yumeng Guo. "l0 regularization based on a prior image incorporated non-local means for limited-angle X-ray CT reconstruction." Journal of X-Ray Science and Technology 26, no. 3 (2018): 481–98. http://dx.doi.org/10.3233/xst-17334.

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