A SURVEY ON MACHINE LEARNING APPROACH TO REDUCE DIMENSIONALITY SPACE IN LARGE DATASETS USING PCA AND LDA

Jessin Shah P A; Dr. G Kiruthiga

A SURVEY ON MACHINE LEARNING APPROACH TO REDUCE DIMENSIONALITY SPACE IN LARGE DATASETS USING PCA AND LDA

June 2021

Vol-7, Issue-3

Paper ID: 14716

ISSN: 2395-4396

Downloads: 0

Abstract & Details

Research Area

Computer Science and Engineering

Keywords

PCA LDA Dimensionality Reduction

Abstract

Due to digitization several sectors like health, web organizations, production, generate huge volume of data. To uncover the patterns among the attributes of this data machine learning algorithms can be used and can make predictions that can be used by the medical practitioners and people at different managerial level to make decisions. All attributes gathered might not be contribute to the prediction and by removing irrelevant attributes reduces the burden on machine learning. There are several techniques to reduce the dimension of data gathered. These dimensional reduction techniques help to reduce the dimension and thereby only features attributes can be used to model the system. This increases the performance of the model. The traditional dimensionality reduction techniques mostly used is Principal Component Analysis and Linear Discriminant Analysis In this paper, different dimensionality reduction techniques and their result on the performance of the machine learning techniques is studied.

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Author Information

#	Name	Institute / Affiliation
1	Jessin Shah P A	IES College of Engineering
2	Dr. G Kiruthiga	IES College of Engineering

How to Cite

Use the following formats to cite this article in your research.

APA Style

A, Jessin Shah P & Kiruthiga, Dr. G (2021). A SURVEY ON MACHINE LEARNING APPROACH TO REDUCE DIMENSIONALITY SPACE IN LARGE DATASETS USING PCA AND LDA. International Journal of Advance Research and Innovative Ideas In Education, 7(3), 2852-2858.

MLA Style

A, Jessin Shah P, and Dr. G Kiruthiga. "A SURVEY ON MACHINE LEARNING APPROACH TO REDUCE DIMENSIONALITY SPACE IN LARGE DATASETS USING PCA AND LDA." International Journal of Advance Research and Innovative Ideas In Education, vol. 7, no. 3, 2021, pp. 2852-2858.

IEEE Style

Jessin Shah P A and Dr. G Kiruthiga, "A SURVEY ON MACHINE LEARNING APPROACH TO REDUCE DIMENSIONALITY SPACE IN LARGE DATASETS USING PCA AND LDA," International Journal of Advance Research and Innovative Ideas In Education, vol. 7, no. 3, pp. 2852-2858, 2021.

Vancouver Style

A Jessin Shah P, Kiruthiga Dr. G. A SURVEY ON MACHINE LEARNING APPROACH TO REDUCE DIMENSIONALITY SPACE IN LARGE DATASETS USING PCA AND LDA. International Journal of Advance Research and Innovative Ideas In Education. 2021;7(3):2852-2858.

Harvard Style

A, Jessin Shah P & Kiruthiga, Dr. G (2021) 'A SURVEY ON MACHINE LEARNING APPROACH TO REDUCE DIMENSIONALITY SPACE IN LARGE DATASETS USING PCA AND LDA', International Journal of Advance Research and Innovative Ideas In Education, 7(3), pp. 2852-2858.

Chicago Style

A, Jessin Shah P and Dr. G Kiruthiga. "A SURVEY ON MACHINE LEARNING APPROACH TO REDUCE DIMENSIONALITY SPACE IN LARGE DATASETS USING PCA AND LDA." International Journal of Advance Research and Innovative Ideas In Education 7, no. 3 (2021): 2852-2858.

Turabian Style

A, Jessin Shah P and Dr. G Kiruthiga. "A SURVEY ON MACHINE LEARNING APPROACH TO REDUCE DIMENSIONALITY SPACE IN LARGE DATASETS USING PCA AND LDA." International Journal of Advance Research and Innovative Ideas In Education 7, no. 3 (2021): 2852-2858.