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High-Dimensional LASSO-Based Computational Regression Models: Regularization, Shrinkage, and Selection

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Original languageEnglish
Pages (from-to)359-383
JournalMachine Learning and Knowledge Extraction
Issue number1
Publication statusPublished - 14 Jan 2019
Publication typeA1 Journal article-refereed


Regression models are a form of supervised learning methods that are important for machine learning, statistics, and general data science. Despite the fact that classical ordinary least squares (OLS) regression models have been known for a long time, in recent years there are many new developments that extend this model significantly. Above all, the least absolute shrinkage and selection operator (LASSO) model gained considerable interest. In this paper, we review general regression models with a focus on the LASSO and extensions thereof, including the adaptive LASSO, elastic net, and group LASSO. We discuss the regularization terms responsible for inducing coefficient shrinkage and variable selection leading to improved performance metrics of these regression models. This makes these modern, computational regression models valuable tools for analyzing high-dimensional problems.

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