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A Review on Dimensionality Reduction Techniques

by Priyanka Jindal, Dharmender Kumar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 173 - Number 2
Year of Publication: 2017
Authors: Priyanka Jindal, Dharmender Kumar
10.5120/ijca2017915260

Priyanka Jindal, Dharmender Kumar . A Review on Dimensionality Reduction Techniques. International Journal of Computer Applications. 173, 2 ( Sep 2017), 42-46. DOI=10.5120/ijca2017915260

@article{ 10.5120/ijca2017915260,
author = { Priyanka Jindal, Dharmender Kumar },
title = { A Review on Dimensionality Reduction Techniques },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2017 },
volume = { 173 },
number = { 2 },
month = { Sep },
year = { 2017 },
issn = { 0975-8887 },
pages = { 42-46 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume173/number2/28311-2017915260/ },
doi = { 10.5120/ijca2017915260 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:20:13.407094+05:30
%A Priyanka Jindal
%A Dharmender Kumar
%T A Review on Dimensionality Reduction Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 173
%N 2
%P 42-46
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Progress in digital data acquisition and storage technology has resulted in exponential growth in high dimensional data. Removing redundant and irrelevant features from this high-dimensional data helps in improving mining performance and comprehensibility and increasing learning accuracy. Feature selection and feature extraction techniques as a preprocessing step are used for reducing data dimensionality. This paper analyses some existing popular feature selection and feature extraction techniques and addresses benefits and challenges of these algorithms which would be beneficial for beginners..

References
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Index Terms

Computer Science
Information Sciences

Keywords

Feature Selection Feature Extraction Principal Component Analysis (PCA) Filter methods Wrapper Methods.

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