Results 31 to 40 of about 215,679 (256)

Machine Learning Regularization Methods in High-Dimensional Monetary and Financial VARs

Mathematics, 2022
Vector autoregressions (VARs) and their multiple variants are standard models in economic and financial research due to their power for forecasting, data analysis and inference. These properties are a consequence of their capabilities to include multiple
Javier Sánchez García, Salvador Cruz Rambaud   +1 more
doaj   +1 more source

An Efficient Sparse Twin Parametric Insensitive Support Vector Regression Model

Mathematics
This paper proposes a novel sparse twin parametric insensitive support vector regression (STPISVR) model, designed to enhance sparsity and improve generalization performance.
Shuanghong Qu, Yushan Guo, Renato De Leone, Min Huang, Pu Li   +4 more
doaj   +1 more source

Non-Linear Regularized Attenuation Compensation for Microwave Breast Imaging

IEEE Open Journal of Signal Processing, 2023
We develop non-linear optimization algorithms for attenuation compensation of rapidly time-varying microwave signals in the context of breast imaging.
Nasser Kazemi, Elise Fear
doaj   +1 more source

Structured Sparsity: Discrete and Convex approaches

, 2015
Compressive sensing (CS) exploits sparsity to recover sparse or compressible signals from dimensionality reducing, non-adaptive sensing mechanisms. Sparsity is also used to enhance interpretability in machine learning and statistics applications: While ...
A. Beck, A. Chambolle, A. Chambolle, A. Gilbert, A. Goldberg, A. Goy, A. Gramfort, A. Nemirovskii, A. Puig, A. Subramanian, B. Efron, B. He, B. McCoy, B. Natarajan, C. Sheppard, D. Bertsekas, D. Donoho, D. Heckerman, D. Needell, F. Girosi, F. Rapaport, G. Nemhauser, G. Nemhauser, H. Zhou, I. Daubechies, I. Johnstone, International Neuroinformatics Coordinating Faculty, J. Bonnans, J. Borwein, J. Dahl, J. Huang, J. Huang, J. Orlin, J. Shapiro, J. Tropp, L. He, M. Born, M. Crouse, M. Fukushima, M. Lustig, M. Stojnic, M. Vincent, N. Simon, P. Combettes, P. Loh, P. Tseng, P. Zhao, Q. Tran-Dinh, R. Baraniuk, R. Baraniuk, R. Baraniuk, R. Jenatton, R. Jenatton, S. Boyd, S. Boyd, S. Chen, S. Foucart, S. Fujishige, S. Fujishige, S. Mallat, S. Mallat, S. Robinson, S. Villa, S. Villa, S. Wright, S. Wright, T. Blumensath, T. Blumensath, V. Chandrasekaran, V. Kolmogorov, W. Gerstner, Y. Bengio, Y. Eldar, Y. Nesterov, Y. Nesterov, Y. Nesterov   +75 more
core   +1 more source

Statistical sparsity

Biometrika, 2018
The main contribution of this paper is a mathematical definition of statistical sparsity, which is expressed as a limiting property of a sequence of probability distributions. The limit is characterized by an exceedance measure~$H$ and a rate parameter~$ > 0$, both of which are unrelated to sample size. The definition is sufficient to encompass all
McCullagh, Peter, Polson, Nicholas
openaire   +2 more sources

DPC-SMOTE Over-sampling Algorithm for Imbalanced Data Classification

Journal of Harbin University of Science and Technology
An oversampling algorithm based on density peak clustering is proposed to solve the problem of noise and imbalance among classes in imbalanced data sets.
LIU Zhihan, ZHANG Zhonglin, ZHAO Lei
doaj   +1 more source

Sparsity Invariant CNNs

, 2017
In this paper, we consider convolutional neural networks operating on sparse inputs with an application to depth upsampling from sparse laser scan data.
Brox, Thomas, Franke, Uwe, Geiger, Andreas, Schneider, Lukas, Schneider, Nick, Uhrig, Jonas   +5 more
core   +1 more source

A Novel Hybrid Item-Based Similarity Method to Mitigate the Effects of Data Sparsity in Multi-Criteria Collaborative Filtering

IEEE Access
Data sparsity presents a significant challenge for Recommendation Systems, particularly in neighborhood-based approaches that rely on co-ratings to compute similarity.
Burcu Demirelli Okkalioglu
doaj   +1 more source

TV Regularized Low-Rank Framework for Localizing Premature Ventricular Contraction Origin

IEEE Access, 2019
Premature ventricular contraction (PVC) can cause great harm to human health. Both invasive and non-invasive techniques for detecting electrical activity of PVC or locating ectopic pacemakers are used in clinical diagnosis.
Lin Fang, Qi Zhuang, Wei Mao, Yunmei Chen, Huafeng Liu   +4 more
doaj   +1 more source

Comparing measures of sparsity [PDF]

2008 IEEE Workshop on Machine Learning for Signal Processing, 2008
Sparsity of representations of signals has been shown to be a key concept of fundamental importance in fields such as blind source separation, compression, sampling and signal analysis. The aim of this paper is to compare several commonlyused sparsity measures based on intuitive attributes.
Hurley, Niall P., Rickard, Scott T.
openaire   +3 more sources