Proceedings of the 2nd International Conference on ICT for Digital, Smart, and Sustainable Development, ICIDSSD 2020, 27-28 February 2020, Jamia Hamdard, New Delhi, India

Research Article

Recent Trends in Dimension Reduction Methods

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  • @INPROCEEDINGS{10.4108/eai.27-2-2020.2303136,
        author={Sehban  Fazili and Jyotsna  Grover and Samar  Wazir and Ila  Mehta},
        title={Recent Trends in Dimension Reduction Methods},
        proceedings={Proceedings of the 2nd International Conference on ICT for Digital, Smart, and Sustainable Development, ICIDSSD 2020, 27-28 February 2020, Jamia Hamdard, New Delhi, India},
        publisher={EAI},
        proceedings_a={ICIDSSD},
        year={2021},
        month={3},
        keywords={principle component analysis linear discriminant analysis generalized discriminant analysis},
        doi={10.4108/eai.27-2-2020.2303136}
    }
    
  • Sehban Fazili
    Jyotsna Grover
    Samar Wazir
    Ila Mehta
    Year: 2021
    Recent Trends in Dimension Reduction Methods
    ICIDSSD
    EAI
    DOI: 10.4108/eai.27-2-2020.2303136
Sehban Fazili1,*, Jyotsna Grover1, Samar Wazir1, Ila Mehta2
  • 1: Department of Computer Science and Engineering School of Engineering Sciences and Technology Jamia Hamdard, New Delhi
  • 2: ECE Department, NSIT, Delhi
*Contact email: sehbanfazili@gmail.com

Abstract

Dimensionality Reduction (DR) techniques helps us to focus on only that data which is essential or important to us. Basically, these techniques help us reduce a feature of a data element. DR is a process that reduces amount of variables into account, on obtaining the group of principle variables. DR is divided into two simpler methods that are feature elimination and feature extraction. The different methods used to reduce the dimensionality are: PCA (Principle component analysis), LDA (Linear discriminant analysis) and GDA (Generalized Discriminant analysis). Reduction of the dimensionality may be linear or nonlinear, depending on the method used.