Why LASSO, Ridge Regression, and EN

Explanation Based on Soft Computing

authored by

Woraphon Yamaka, Hamza Alkhatib, Ingo Neumann, Vladik Kreinovich

Abstract

In many practical situations, observations and measurement results are consistent with many different models–i.e., the corresponding problem is ill-posed. In such situations, a reasonable idea is to take into account that the values of the corresponding parameters should not be too large; this idea is known as regularization. Several different regularization techniques have been proposed; empirically the most successful are LASSO method, when we bound the sum of absolute values of the parameters, ridge regression method, when we bound the sum of the squares, and a EN method in which these two approaches are combined. In this paper, we explain the empirical success of these methods by showing that these methods can be naturally derived from soft computing ideas.

Organisation(s)

Geodetic Institute

External Organisation(s)

Chiang Mai University
University of Texas at El Paso

Type

Conference contribution

Pages

123-130

No. of pages

8

Publication date

27.07.2021

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Artificial Intelligence

Electronic version(s)

https://scholarworks.utep.edu/cs_techrep/1465/ (Access: Open)
https://doi.org/10.1007/978-3-030-77094-5_12 (Access: Closed)

 

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