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

Abdulhussain, MI and Gan, JQ (2015) An experimental investigation on PCA based on cosine similarity and correlation for text feature dimensionality reduction. In: UNSPECIFIED, ? - ?.

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Abstract

© 2015 IEEE. 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
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Jim Jamieson
Date Deposited: 24 Nov 2015 13:53
Last Modified: 17 Aug 2017 17:30
URI: http://repository.essex.ac.uk/id/eprint/15516

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