Robust Principal Component Analysis based on Fuzzy Coded Data

Year 2017, Volume: 18 Issue: 3, 754 - 762, 30.09.2017

B. Barış Alkan , Sevgi Ganık

https://doi.org/10.18038/aubtda.317765

Abstract

In the presence of outliers in the dataset, the principal component analysis method, like
many of the classical statistical methods, is severely affected. For this
reason, if there are outliers in dataset, researchers tend to use alternative
methods.
Use of fuzzy and robust approaches is the leading choice among these methods. In this study, a new approach to
robust fuzzy principal component analysis is proposed. This approach
combines the power of both robust and fuzzy methods at the same time and
collects these two approaches under the framework of principal component
analysis. The performance of proposed approach called robust principal
component analysis based on fuzzy coded data is examined through a set of
artificial dataset that are generated by considering three different scenarios
and a real dataset to
observe how it is affected by the increase in sample size and changes in the
rate of outliers. In light of the study's findings, it is seen that the
proposed approach gives better results than the ones in the classical and
robust principal component analysis in the presence of outliers in dataset.

Keywords

Fuzzy coded data, Outliers

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