Dimension Reduction Using Principal Component Analysis for Pharmaceutical Domain Deepa N*, Ravi Chandrasekar Assistant Professor, School of Information Technology and Engineering, VIT University, Vellore, Tamil Nadu, India *Corresponding Author E-mail: deepa.rajesh@vit.ac.in
Online published on 12 January, 2017. Abstract Huge number of researchers work in engineering, remote sensing, agriculture, economics, biology domains nowa-days. The major challenges they face are huge number of observations and simulations they collect for their research work. Advanced techniques in data collection and storage capabilities lead to data overload. Due to the increase in the number of observations, some statistical methods fail partially. The dimension of data is nothing but the number of input variables in each observation. One of the present Mathematical challenges is the high dimensional dataset used for research purpose. Researcher may not be able to predict the importance of the variables measured on each observation. We need a mathematical technique which can reduce the dimension of the data. There are many statistical data reduction techniques currently in use viz., Singular Value Decomposition (SVD), Mahalanobis Taguchi Method, Principal Component Analysis, Factor Analysis. Among these, Principal Component Analysis is Multivariate statistical unsupervised dimension reduction technique most popularly used now-a-days because of its simplicity. In this paper, a sample dataset has been taken and by applying Principal Component analysis, dimension of the data has been reduced. The results of MATLAB program written for Principal Component Analysis by applying the sample dataset is given in this paper. Top Keywords Dimension Reduction, Principal Component Analysis, Factor Analysis, unsupervised Data Reduction. Top |