Authorship Attribution Using Principal Component Analysis and Competitive Neural Networks

Mathematical and Computational Applications

Can, Mehmet¹ 

关键词: principal components;    authorship attribution;    stylometry;    text categorization;    stylistic features;    syntactic characteristics;    multilayer preceptor;    competitive learning;    artificial neural network;   

DOI  :  10.3390/mca19010021

学科分类：计算数学

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY