Abstract
Graphical approaches are widely used in the examination of multivariate data. The most popular of them is called Biplot. This technique provides an geometric approach that examined the relations between observations and variables in the principal components space with reduced-size. Principal component analysis (PCA) is obtained by covariance (or corelation) matrix. Therefore it is influenced by the presence of outliers. PCA biplot is used for visualization of PCA results. In this study, we compare the performances of PCA biplots based on different robust cavariance matrix estimates on the one real and the artificial data sets. Results indicate that Robust PCA biplot is preferred to instead of Classical PCA biplot in the presence of outliers.
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Alkan, B.B., Atakan, C. (2014). Comparison of Principal Component Analysis Biplots Based on Different Robust Covariance Matrix Estimates. In: Banerjee, S., Erçetin, Ş. (eds) Chaos, Complexity and Leadership 2012. Springer Proceedings in Complexity. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-7362-2_5
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DOI: https://doi.org/10.1007/978-94-007-7362-2_5
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