Results 1 to 10 of about 19,992 (145)

Statistical Significance of Clustering using Soft Thresholding. [PDF]

J Comput Graph Stat, 2015
Clustering methods have led to a number of important discoveries in bioinformatics and beyond. A major challenge in their use is determining which clusters represent important underlying structure, as opposed to spurious sampling artifacts. This challenge is especially serious, and very few methods are available, when the data are very high in ...
Huang H, Liu Y, Yuan M, Marron JS.
europepmc   +5 more sources

Defining Orientation of the Foot in WBCT using Hounsfield Units Analysis: Do Soft Tissues Skew the Measurements? [PDF]

Foot & Ankle Orthopaedics
Research Type: Level 4 – Case series Introduction/Purpose: Defining orientation axes of the foot typically requires full segmentation of bones from volumetric data, followed by geometric primitive fitting (GPF) or principal component analysis (PCA). This
Martim Pinto MD, Wolfram Grün MD, Emily Luo MHSc, Enrico Pozzessere MD, Antoine Acker MD, Erik Huanuco Casas MMed(Orth), Mark Easley MD, Cesar De Cesar Netto MD, PhD, Francois Lintz MD, PhD   +8 more
doaj   +2 more sources

OPERA net Otsu driven performance enhanced image restoration algorithm [PDF]

Scientific Reports
Digital images have progressed significantly in many areas but various types of noise still exist in real-world images such as Gaussian noise, Poisson noise, Salt-and-pepper noise so on.
Pallvi Sharma, Priyanka Jarial, Bhim Sain Singla   +2 more
doaj   +2 more sources

Soft Threshold Ternary Networks [PDF]

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020
Large neural networks are difficult to deploy on mobile devices because of intensive computation and storage. To alleviate it, we study ternarization, a balance between efficiency and accuracy that quantizes both weights and activations into ternary values.
Xu, Weixiang, He, Xiangyu, Zhao, Tianli, Hu, Qinghao, Wang, Peisong, Cheng, Jian   +5 more
openaire   +2 more sources

Image De-Noising Based On Wavelet Transform and Block Matching

Tikrit Journal of Pure Science, 2023
This paper suggested a de-noising algorithm used in grayscale images. As long as the noisy image does not give the desired view of its features, de-noising is required. The algorithm is based on block matching and wavelet transformation.
Ahmed Abdulmunem Hussein, Mohammed Khawwam Ahmed   +1 more
doaj   +1 more source

Representation Learning via Cauchy Convolutional Sparse Coding

IEEE Access, 2021
In representation learning, Convolutional Sparse Coding (CSC) enables unsupervised learning of features by jointly optimising both an $\ell _{2}$ -norm fidelity term and a sparsity enforcing penalty.
Perla Mayo, Oktay Karakus, Robin Holmes, Alin Achim   +3 more
doaj   +1 more source

Linear Convergence of Iterative Soft-Thresholding [PDF]

Journal of Fourier Analysis and Applications, 2008
In this article a unified approach to iterative soft-thresholding algorithms for the solution of linear operator equations in infinite dimensional Hilbert spaces is presented. We formulate the algorithm in the framework of generalized gradient methods and present a new convergence analysis.
Bredies, Kristian, Lorenz, Dirk A.
openaire   +2 more sources

Using Wavelet Shrinkage in the Cox Proportional Hazards Regression model (simulation study) [PDF]

المجلة العراقية للعلوم الاحصائية, 2022
The proposed method in this paper dealt with the problem of data contamination in the Cox Proportional Hazards Regression model (CPHRM) by using Wavelet Shrinkage to de-noise data, calculating the discrete wavelet transformation coefficients for wavelets
taha ali, Jwana Rostam Qadir
doaj   +1 more source

Image Denoising Algorithm Based on Gradient Domain Guided Filtering and NSST

IEEE Access, 2023
Traditional image denoising methods, which do not depend on data training, have good interpretability. However, traditional image denoising methods hardly achieve the denoising effect of deep learning methods.
Zhe Li, Hualin Liu, Libo Cheng, Xiaoning Jia   +3 more
doaj   +1 more source

Between hard and soft thresholding: optimal iterative thresholding algorithms [PDF]

Information and Inference: A Journal of the IMA, 2019
AbstractIterative thresholding algorithms seek to optimize a differentiable objective function over a sparsity or rank constraint by alternating between gradient steps that reduce the objective and thresholding steps that enforce the constraint. This work examines the choice of the thresholding operator and asks whether it is possible to achieve ...
Liu, Haoyang, Barber, Rina Foygel
openaire   +2 more sources