Is PCA robust against outliers?
Principal Component Analysis (PCA) is a very versatile technique for dimension reduction in multivariate data. Classical PCA is very sensitive to outliers and can lead to misleading conclusions in the presence of outliers.
Should I remove outliers for PCA?
1 Answer. As a very general rule, the proper treatment of outliers depend on the analysis purpose – if you’re looking for large-scale tendencies, they often better be removed, but sometimes your goal might be actually finding the non-typical data points.
Why is PCA used to detect outliers?
On Why is PCA sensitive to outliers? it is explained why it is sensitive to Outliers, this can probably used as a tool, as well. In other words: How exactly is PCA used to detect outliers respectively how are they detected after performing the PCA?
How are outliers detected in high dimensional data?
High-dimensional data are particularly challenging for outlier detection. Robust PCA methods have been developed to build models that are unaffected by outliers in high dimensions. These outliers are generally characterized by their deviation from the PCA subspace.
How is PCA used in statistical data analysis?
PCA is thus frequently considered a first step of a statistical data analysis which aims at compression of the data: decreasing their dimensionality without losing much information. Further analysis on the principal components can consist of various methods, such as clustering, discriminant analysis, regression, etc.
How to perform principal component analysis ( PCA )?
Two approaches are presented to perform principal component analysis (PCA) on data which contain both outlying cases and missing elements. At first an eigendecomposition of a covariance matrix which can deal with such data is proposed, but this approach is not fit for data where the number of variables exceeds the number of cases.