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An experimental investigation on PCA based on cosine similarity and correlation for text feature dimensionality reduction

Abdulhussain, Maysa I and Gan, John Q (2015) An experimental investigation on PCA based on cosine similarity and correlation for text feature dimensionality reduction. In: 2015 7th Computer Science and Electronic Engineering (CEEC), 2015-09-24 - 2015-09-25.

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Abstract

Principal component analysis (PCA) is a commonly used method for feature extraction and dimensionality reduction. This paper proposes PCA based on similarity/correlation criteria instead of covariance to gain low-dimensional features with high performance in text classification. Experimental results have demonstrated the advantages and usefulness of the proposed method in text classification in high-dimensional feature space, in terms of the number of features required to achieve the best classification accuracy.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: 2015 7th Computer Science and Electronic Engineering Conference, CEEC 2015 - Conference Proceedings
Uncontrolled Keywords: PCA; dimensionality-reduction; text categorization
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 24 Nov 2015 13:53
Last Modified: 15 Jan 2022 00:26
URI: http://repository.essex.ac.uk/id/eprint/15516

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