Research Article
Recent Trends in Dimension Reduction Methods
@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
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.
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