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Hard Threshold Least Mean Squares Algorithm [article]

Lampros Flokas, Petros Maragos
2016 arXiv   pre-print
This work presents a new variation of the commonly used Least Mean Squares Algorithm (LMS) for the identification of sparse signals with an a-priori known sparsity using a hard threshold operator in every  ...  It examines some useful properties of the algorithm and compares it with the traditional LMS and other sparsity aware variations of the same algorithm.  ...  INTRODUCTION L EAST Mean Squares Algorithm (LMS), introduced by Widrow and Hoff [1] , is an algorithm used in many signal processing tasks like adaptive system identification.  ... 
arXiv:1608.01128v1 fatcat:ykdepnp2drgjra7rut3clqlaay

Iterative-Promoting Variable Step Size Least Mean Square Algorithm for Accelerating Adaptive Channel Estimation [article]

Beiyi Liu, Guan Gui, Li Xu, Nobuhiro Shimoi
2015 arXiv   pre-print
Invariable step size based least-mean-square error (ISS-LMS) was considered as a very simple adaptive filtering algorithm and hence it has been widely utilized in many applications, such as adaptive channel  ...  In this paper, we propose an iterative-promoting variable step size based least-mean-square error (VSS-LMS) algorithm to control the convergence speed as well as to improve the estimation performance.  ...  , e.g., VSS-LMS [6] [13] and normalized least mean fourth algorithm (NLMF)[10] [11] .  ... 
arXiv:1501.07107v1 fatcat:wbazzhkiavbt5k62by5ho7o6v4

Denoising Electrocardiogram Signal from Electromyogram Noise Using Adaptive Filter Combination

Anissa Khiter, Amel Mitiche, Lahcene Mitiche
2020 Revue d'intelligence artificielle : Revue des Sciences et Technologies de l'Information  
This paper introduces a new filter that take the output of wavelet wiener based filter to design a normalized Least Mean Square (NLMS) based filter in order to reduce the broadband mypotentials (EMG) noise  ...  In addition, the combination shows better results through increasing SNR and reducing Mean Square Error (MSE) compared to other existing techniques.  ...  mean square algorithm.  ... 
doi:10.18280/ria.340109 fatcat:v34gvt3hffd35aeoiocexm7shu

Adaptive Wavelet Based MRI Brain Image De-noising

Noorbakhsh Amiri Golilarz, Hui Gao, Rajesh Kumar, Liaqat Ali, Yan Fu, Chun Li
2020 Frontiers in Neuroscience  
In TNN and optimized based image de-noising, it was required to use Least-mean-square (LMS) learning and optimization algorithms, respectively to find the optimum threshold value and parameters of the  ...  De-noising using improved AGGD threshold function provides better results in terms of Peak Signal to Noise Ratio (PSNR) and also faster processing time since there is no need to use any Least-mean-square  ...  In TNN and optimized based image de-noising, it was required to use Least-mean-square (LMS) learning and optimization algorithms, respectively to find the optimum threshold value and parameters of the  ... 
doi:10.3389/fnins.2020.00728 pmid:32774240 pmcid:PMC7388743 fatcat:rfvib3ktonfctj2u5uevbphmti

Iterative-Promoting Variable Step-size Least Mean Square Algorithm For Adaptive Sparse Channel Estimation [article]

Beiyi Liu, Guan Gui, Li Xu
2015 arXiv   pre-print
Least mean square (LMS) type adaptive algorithms have attracted much attention due to their low computational complexity.  ...  However, these proposed algorithms may hard to make tradeoff between convergence speed and estimation performance with only one step-size.  ...  Adachi, "Improved least mean square algorithm with application to adaptive sparse channel estimation," EURASIP J. Wirel. Commun. Netw., vol. 2013, no. 1, p. 204, 2013. [2] K. Pelekanakis and M.  ... 
arXiv:1504.03077v1 fatcat:vjqbjimuzvbutaiha4j35lr3wy

On the Infeasibility of Training Neural Networks with Small Squared Errors

Van H. Vu
1997 Neural Information Processing Systems  
We will prove for several classes F of neural networks that achieving a relative error smaller than some fixed positive threshold (independent from the size of the data set) is NP-hard.  ...  We demonstrate that the problem of training neural networks with small (average) squared error is computationally intractable.  ...  By efficiency we mean an algorithm terminating in polynomial time (polynomial in the size of the input).  ... 
dblp:conf/nips/Vu97 fatcat:pthiyeeg5ja43p7u5vfydu52cu

Best Fit Wavelet Function for Path Loss Prediction in Wireless Communication System

Kishor K., S.K. Bodhe
2016 International Journal of Computer Applications  
Different threshold levels have been tested to find the mean square error (MSE) due to reconstructed path loss after compression.  ...  Hard thresholding is used to compress these coefficients as much as possible.  ...  Different threshold levels have been applied to find RMSE (Root Mean Squared error) due to the reconstructed path loss.  ... 
doi:10.5120/ijca2016908585 fatcat:7gyrkbqlrfewljgv6gzoc5xlja

Interval Set Clustering of Web Users with Rough K-Means

Pawan Lingras, Chad West
2004 Journal of Intelligent Information Systems  
threshold Relative threshold in rough k-means algorithms (threshold >= 1.0). Default: threshold = 1.5.  ...  threshold Relative threshold in rough k-means algorithms (threshold >= 1.0). Default: threshold = 1.5.  ... 
doi:10.1023/b:jiis.0000029668.88665.1a fatcat:x465oykeyfajdjvndibehfrblu

Signal Enhancement Algorithm for On-line Detection of Multi-metal Ions Based on Ultraviolet-visible Spectroscopy

Fengbo Zhou, Yonggang Li, Hongqiu Zhu, Can Zhou, Changgeng Li
2020 IEEE Access  
INDEX TERMS Zinc hydrometallurgy, signal enhancement algorithm, partial least squares, on-line detection, optimal threshold parameter, ultraviolet-visible spectroscopy. 16000 This work is licensed under  ...  Finally, the proposed adaptive algorithm is used for spectral signal preprocessing, and partial least squares regression is used for spectral signal modeling analysis.  ...  Compared to the results of a partial least squares regression without spectral data pretreatment, the proposed signal enhancement algorithm combined with partial least squares regression significantly  ... 
doi:10.1109/access.2020.2967021 fatcat:pk55bvmx4jeo7iih6jnezt5bfy

An Algorithm of Suppressing Image Noise Based on Wavelet Threshold Function and Improved Median Filtering

Liang GAI
2017 DEStech Transactions on Computer Science and Engineering  
Experimental results show that the peak signal to noise ratio (PSNR) is increased, the mean square error (MSE) is decreased and the image detail information is better preserved.  ...  For the presence of salt-and-pepper noise and Gaussian noise in image processing, an algorithm of suppressing image noise based on the wavelet threshold function and improved median filter is proposed.  ...  The other algorithms, including noise suppression algorithm based on switching median filter and bayesian least square -gaussian scale mixture model [2] [3] [4] , suppressing image mixed noise algorithm  ... 
doi:10.12783/dtcse/cmee2016/5338 fatcat:vjzmakusifb4hldqlqoorsfxbe

Performance Study of Different Denoising Methods for ECG Signals

Mohammed AlMahamdy, H. Bryan Riley
2014 Procedia Computer Science  
These algorithms are: discrete wavelet transform (universal and local thresholding), adaptive filters (LMS and RLS), and Savitzky-Golay filtering.  ...  Fig. 8 . 8 (a) The Percentage Root-mean-square Difference. (b) Estimated SNR.  ...  This adjustment is mainly achieved by: 0 ( ) ( ) ( ) N T i k k i y k w k s k i W X (3) Least Mean Squares (LMS) Because of the important features of LMS: the simplicity [21] and relatively fewer computational  ... 
doi:10.1016/j.procs.2014.08.048 fatcat:2clfkkegnva3doednyirwetuuq

Speckle Reduction of Synthetic Aperture Radar Images using Median Filter and Savitzky-Golay Filter

Ruchita Gir, Lalit Jain, Rajesh Rai
2015 International Journal of Computer Applications  
Savitzky-Golay filter minimize the least-squares error by fitting a polynomial to frames of noisy data.  ...  Mean Square Error (MSE) MSE indicates average square difference of the pixels throughout the image between the original image (speckled) g(x,y) and Despeckled image f(x,y).  ... 
doi:10.5120/19874-1877 fatcat:myka5yb3ajdhlagyakji5gyo4q

No penalty no tears: Least squares in high-dimensional linear models [article]

Xiangyu Wang, David Dunson, Chenlei Leng
2016 arXiv   pre-print
For these problems, we advocate the use of a generalized version of OLS motivated by ridge regression, and propose two novel three-step algorithms involving least squares fitting and hard thresholding.  ...  Ordinary least squares (OLS) is the default method for fitting linear models, but is not applicable for problems with dimensionality larger than the sample size.  ...  These algorithms involve least squares type of fitting and hard thresholding, and are non-iterative in nature.  ... 
arXiv:1506.02222v5 fatcat:ogqax7uln5g7ho6biexumjsg3e

Optimum Design and Application of Nano-Micro-Composite Ceramic Tool and Die Materials with Improved Back Propagation Neural Network [chapter]

Chonghai Xu, Jingjie Zhang, Mingdong Yi
2011 Artificial Neural Networks - Industrial and Control Engineering Applications  
And the mean square error MSE is 1.24.  ...  The mean square error MSE is 1.05 and the elapsed-time is 144.20s.  ...  Therefore, the structure of BP neural network is 2×6×3 and the last 9 parameters are the threshold values.  ... 
doi:10.5772/16191 fatcat:n4trg7yucvblddnsaloclyh4mi

High Dimensional Robust M-Estimation: Arbitrary Corruption and Heavy Tails [article]

Liu Liu, Tianyang Li, Constantine Caramanis
2019 arXiv   pre-print
response variables, a near linear-time computable trimmed gradient estimator satisfies the RDC, and hence Robust Hard Thresholding is minimax optimal.  ...  We define a natural condition we call the Robust Descent Condition (RDC), and show that if a gradient estimator satisfies the RDC, then Robust Hard Thresholding (IHT using this gradient estimator), is  ...  To address this issue, we propose Trimmed Hard Thresholding (Algorithm 1), which uses hard thresholding after each trimmed gradient update 2 .  ... 
arXiv:1901.08237v2 fatcat:6wnfahlsi5e3zkaxgxfgwkbhwy
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