Principal Component Analysis for Outlier Detection

Authors

  • S. Stephen Raj
  • K. Senthamarai Kannan
  • K. Manoj

DOI:

https://doi.org/10.37591/rrjost.v7i1.830

Abstract

Principal Components Analysis (PCA) is one of the well-known and frequently used multivariate exploratory data analyses. PCA is concerned with analyzing and understanding data in high dimensions, that is to say, PCA method analyzes data sets that represent observations which are identified by several dependent variables that are inter-correlated. Multidimensional scaling (MDS) is a technique that makes a map displaying the comparative attitudes of a number of objects or cases, which corresponds to the table of distances between them. The process of identifying outliers is an interesting and important aspect in the analysis of data. The objective of this work is to establish the effects of the outliers in PCA for multidimensional scaling techniques and the computed results are obtained from the simulated data.

 

Keywords: Outliers, PCA, MDS, multivariate analysis, robust statistics

Cite this Article

Stephen Raj S, Senthamarai Kannan K, Manoj K. Principal Component Analysis for Outlier Detection. Research & Reviews: Journal of Statistics. 2018; 7(1): 62s–68sp.

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Published

2018-05-17

Issue

Vol. 7 No. 1 (2018)

Section

Research Article

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