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Mathematical and Computational Applications is published by MDPI from Volume 21 Issue 1 (2016). Articles in this Issue were published by another publisher in Open Access under a CC-BY (or CC-BY-NC-ND) licence. Articles are hosted by MDPI on mdpi.com as a courtesy and upon agreement with the previous journal publisher.
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Math. Comput. Appl. 2014, 19(1), 21-36; https://doi.org/10.3390/mca19010021

Authorship Attribution Using Principal Component Analysis and Competitive Neural Networks

International University of Sarajevo, Faculty of Engineering and Natural Sciences Hrasnićka Cesta 15, 71000 Sarajevo, Bosnia and Herzegovina
Published: 1 April 2014
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Abstract

Feature extraction is a common problem in statistical pattern recognition. It refers to a process whereby a data space is transformed into a feature space that, in theory, has exactly the same dimension as the original data space. However, the transformation is designed in such a way that the data set may be represented by a reduced number of "effective" features and yet retain most of the intrinsic information content of the data; in other words, the data set undergoes a dimensionality reduction. Principal component analysis is one of these processes. In this paper the data collected by counting selected syntactic characteristics in around a thousand paragraphs of each of the sample books underwent a principal component analysis. Authors of texts identified by the competitive neural networks, which use these effective features.
Keywords: principal components; authorship attribution; stylometry; text categorization; stylistic features; syntactic characteristics; multilayer preceptor; competitive learning; artificial neural network principal components; authorship attribution; stylometry; text categorization; stylistic features; syntactic characteristics; multilayer preceptor; competitive learning; artificial neural network
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).
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Can, M. Authorship Attribution Using Principal Component Analysis and Competitive Neural Networks. Math. Comput. Appl. 2014, 19, 21-36.

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