Principal Component Analysis for Authorship Attribution

Abstract

A common problem in statistical pattern recognition is that of feature selection or feature extraction. Feature selection 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. In this paper the data collected by counting words and characters in around a thousand paragraphs of each sample book underwent a principal component analysis performed using neural networks. Then first of the principal components is used to distinguished the books authored by a certain author.

Feature Selection and Radial Basis Function Network for Parkinson Disease Classification

“…Positive instability, or poor balance and coordination. These symptoms also become more noticeable [4,5]. Parkinson's disease can't be diagnosed easily in the early stages since there are many factors to analyze.…”

“…In the Initial of the disease, the most noticeable symptoms are shaking, stiffness and slow movement [4]. [4].…”

“…In addition, more than a third of people with PD commonly feel with depression and anxiety [5]. There are further symptoms contain sensory, sleep, and emotional problems [4,5]. Major motor symptoms are collectively called "parkinsonian syndrome" [6].…”

“…The motor symptoms of the disease are caused by cells death in the nigra, a region of the brain's middle. This leads to inadequate dopamine in these areas [4]. Generally, the cause of these death of cells is not understood but contains the growth of proteins in Lewy bodies in neurons [7].…”

Authorship Attribution Using Principal Component Analysis and Nearest Neighbor Rule for Neural Networks

“…Recently, neural networks are used on a wide variety of features (Graham et al 2005, Zheng et. al 2006, 2012, Can, Hadziabdic, and Demir 2011. Some researchers used the techniques of k-nearest neighbor (Kjell et al 1995, Hoom et al 1999, Zhao, and Zobel 2005, naïve Bayes , Hoom et al 1999, Peng et al 2004, rule learners (Binongo 2003, Holmes 2003, Graham et al 2005, Argamon-Engelson et.…”