Mathematical and Computational Applications | |
Authorship Attribution Using Principal Component Analysis and Competitive Neural Networks | |
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
【 预 览 】
Files | Size | Format | View |
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RO201902026682002ZK.pdf | 682KB | download |