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Sparse and robust estimation with ridge minimax concave penalty
- Source :
- Information Sciences. 571:154-174
- Publication Year :
- 2021
- Publisher :
- Elsevier BV, 2021.
-
Abstract
- Feature selection is an important procedure that is used in data mining to extract valuable information from large quantities of data. Existing penalization methods use a single penalty function to select important features. However, these methods do not yield sufficiently accurate predictions and selection outcomes. Therefore, construction of a concise and efficient prediction model would be beneficial. In this study, we propose a novel penalty function using a ridge and minimax concave penalty to overcome the limitations of individual penalty functions. Furthermore, we introduce a robust penalized feature selection method with Huber loss function, which is implemented by a local approximation algorithm. The theoretical properties of the algorithm have been described. Simulated and real-world data analyses are used to demonstrate the efficacy of the proposed method.
- Subjects :
- Estimation
Mathematical optimization
Information Systems and Management
Computer science
Approximation algorithm
Feature selection
Ridge (differential geometry)
Minimax
Computer Science Applications
Theoretical Computer Science
Huber loss
Artificial Intelligence
Control and Systems Engineering
Penalty method
Software
Selection (genetic algorithm)
Subjects
Details
- ISSN :
- 00200255
- Volume :
- 571
- Database :
- OpenAIRE
- Journal :
- Information Sciences
- Accession number :
- edsair.doi...........60d4931f5f5ae2d57d3dc0298203401a