Authorship Attribution by Functional Discriminant Analysis

  • Chahrazed KettafEmail author
  • Abderrahmane Yousfate
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11989)


Recognizing the author of a given text is a very difficult task that relies on several complicated and correlated criterias. For this purpose, several classification methods are used (neuronal network, discriminant analysis, SVM...). But a good representation of the text that keeps the maximum of the stylistic information is very important and has a considerable influence on the result. In this paper, we will tackle the problem of the authorship attribution for very long texts using the discriminant analysis extended to the functional case after presenting the texts as elements of a separable Hilbert space.


Authorship attribution Textmining Big textual data Discriminant analysis Funtional classification Functional data analysis 


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Laboratoire de MathématiquesDjillali Liabes UniversitySidi Bel AbbesAlgeria

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