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A Review on Dimensionality Reduction Techniques

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

Priyanka Jindal and Dharmender Kumar. A Review on Dimensionality Reduction Techniques. International Journal of Computer Applications 173(2):42-46, September 2017. BibTeX

@article{10.5120/ijca2017915260,
	author = {Priyanka Jindal and Dharmender Kumar},
	title = {A Review on Dimensionality Reduction Techniques},
	journal = {International Journal of Computer Applications},
	issue_date = {September 2017},
	volume = {173},
	number = {2},
	month = {Sep},
	year = {2017},
	issn = {0975-8887},
	pages = {42-46},
	numpages = {5},
	url = {http://www.ijcaonline.org/archives/volume173/number2/28311-2017915260},
	doi = {10.5120/ijca2017915260},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, 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..

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Keywords

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