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Introduction
Published in Angshul Majumdar, Compressed Sensing for Engineers, 2018
In the elastic-net regularization, the l1-norm and l2-norm have opposing effect. The l1-norm enforces a sparse solution, whereas the l2-norm promotes density. Owing to these opposing effects, it is called “elastic” net. The net effect is that it selects only a few factors, but the grouping effect of l2-norm selects all the correlated factors as well.
Classical Statistics and Modern Machine Learning
Published in Mark Chang, Artificial Intelligence for Drug Development, Precision Medicine, and Healthcare, 2020
Elastic net regularization tends to have a grouping effect, where as correlated input features are assigned equal weights. Elastic net regularization is commonly used in practice and is implemented in many machine learning libraries.
Regression
Published in A. C. Faul, A Concise Introduction to Machine Learning, 2019
Ridge regression is implemented in MATLAB as ridge, while LASSO is implemented as lasso. The latter also incorporates elastic net regularization, since the elastic net becomes LASSO for λ = 1.
Detection and Classification of Corrosion-related Damage Using Solitary Waves
Published in Research in Nondestructive Evaluation, 2022
Hoda Jalali, Ritesh Misra, Samuel J. Dickerson, Piervincenzo Rizzo
The logistic regression is an extension of the linear regression models that predicts the probabilities for classification problems. In this study, the Elastic-net regularized generalized linear (GLMNET) method was used, which includes regularization parameters or penalty terms in order to reduce the complexity of the model and prevent overfitting [51]. In ridge regularization, the regression coefficients are constrained [52], while in the lasso regularization, the regression coefficients of the less important variables are pushed to zero (feature selection) [53]. In this study, the elastic-net regularization method was used, which is a combination of the ridge and the lasso methods with two hyperparameters: α and λ. Here, α indicates whether the model is more like lasso (α = 1) or ridge (α = 0), and λ represents the power of the penalty terms.
An inertial relaxed CQ algorithm with an application to the LASSO and elastic net
Published in Optimization, 2021
In statistics and machine learning, least absolute shrinkage and selection operator (LASSO) is a regression analysis method that performs both variable selection and regularization in order to enhance the prediction accuracy and interpretability of the statistical model it produces. It was originally introduced by Tibshirani in [18] who coined out the term and provided further insights into the observed performance. Subsequently, a number of LASSO variants have been created in order to remedy certain limitations of the original technique and to make the method more useful for particular problems. Among them, elastic net regularization adds an additional ridge regression-like penalty which improves performance when the number of predictors is larger than the sample size, allows the method to select strongly correlated variables together, and improves overall prediction accuracy. More specifically, the LASSO is a regularized regression method with the penalty; while the elastic net is a regularized regression method that linearly combines the and penalties of the LASSO and ridge methods. Here the penalty is defined as .
Hybrid Deep Neural Network-Based Text Representation Model to Improve Microblog Retrieval
Published in Cybernetics and Systems, 2020
Ibtihel Ben Ltaifa, Lobna Hlaoua, Lotfi Ben Romdhane
In detail, we suppose the original set of contextual features denoted as C, the goal of the regularization step is to find a subset of features S to optimize and maximize the performance of the learning model. The regularization method used in the present study is called Elastic Net regularization which combines the L1 regularization and the L2 regularization.