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

Tutkimustuotosvertaisarvioitu

Standard

High-Dimensional LASSO-Based Computational Regression Models: Regularization, Shrinkage, and Selection. / Emmert-Streib, Frank; Dehmer, Matthias.

julkaisussa: Machine Learning and Knowledge Extraction, Vuosikerta 1, Nro 1, 14.01.2019, s. 359-383.

Tutkimustuotosvertaisarvioitu

Harvard

Emmert-Streib, F & Dehmer, M 2019, 'High-Dimensional LASSO-Based Computational Regression Models: Regularization, Shrinkage, and Selection', Machine Learning and Knowledge Extraction, Vuosikerta. 1, Nro 1, Sivut 359-383. https://doi.org/10.3390/make1010021

APA

Vancouver

Author

Emmert-Streib, Frank ; Dehmer, Matthias. / High-Dimensional LASSO-Based Computational Regression Models: Regularization, Shrinkage, and Selection. Julkaisussa: Machine Learning and Knowledge Extraction. 2019 ; Vuosikerta 1, Nro 1. Sivut 359-383.

Bibtex - Lataa

@article{56b02980e77f4321b6a6cbc466b5e091,
title = "High-Dimensional LASSO-Based Computational Regression Models: Regularization, Shrinkage, and Selection",
abstract = "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.",
author = "Frank Emmert-Streib and Matthias Dehmer",
year = "2019",
month = "1",
day = "14",
doi = "10.3390/make1010021",
language = "English",
volume = "1",
pages = "359--383",
journal = "Machine Learning and Knowledge Extraction",
issn = "2504-4990",
publisher = "MDPI",
number = "1",

}

RIS (suitable for import to EndNote) - Lataa

TY - JOUR

T1 - High-Dimensional LASSO-Based Computational Regression Models: Regularization, Shrinkage, and Selection

AU - Emmert-Streib, Frank

AU - Dehmer, Matthias

PY - 2019/1/14

Y1 - 2019/1/14

N2 - 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.

AB - 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.

U2 - 10.3390/make1010021

DO - 10.3390/make1010021

M3 - Article

VL - 1

SP - 359

EP - 383

JO - Machine Learning and Knowledge Extraction

JF - Machine Learning and Knowledge Extraction

SN - 2504-4990

IS - 1

ER -