Dimension Reduction Techniques for Optimization
Dimension-reduction techniques can be used to optimize a class problems. We investigate various dimensional reduction techniques to find the best method for our data processing. It is thoroughly discussed how features are extracted and selected. We discuss the various factors that must be considered when reducing dimensions in this paper. In the later part of this paper, we discuss in detail the basic techniques such as principal component analysis, kernel principal component analysis and auto encoder.
Authors
- Rajiv Pandey (ORCID: https://orcid.org/0000-0003-4849-775X)
- Neeraj Kumar Singh (ORCID: https://orcid.org/0000-0002-1124-0179)
- Abhishek Saxena (ORCID: https://orcid.org/0000-0003-0595-2118)
Institutions
- Université Toulouse III - Paul Sabatier (FR)
- Institut National Polytechnique de Toulouse (FR)
- Université Toulouse-I-Capitole (FR)
- Institut de Recherche en Informatique de Toulouse (FR)
- Amity University (AE)
- Université Toulouse - Jean Jaurès (FR)
- Institut Polytechnique de Bordeaux (FR)
- École Nationale Supérieure d'Électrotechnique, d'Électronique, d'Informatique, d'Hydraulique et des Télécommunications (FR)
Publication Details
- Published
- 2024-10-24
- DOI
- https://doi.org/10.1109/iccsc62048.2024.10830306
- Primary Topic
- Industrial Vision Systems and Defect Detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00