Robust Principal Component Analysis for Power Transformed Compositional Data
Geochemical surveys collect sediment or rock samples, measure the concentration of chemical elements, and report these typically either in weight percent or in parts per million (ppm). There are usually a large number of elements measured and the distributions are often skewed, containing many potential outliers. We present a new robust principal component analysis (PCA) method for geochemical survey data, that involves first transforming the compositional data onto a manifold using a relative...[Show more]
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|Source:||Journal of the American Statistical Association|
|01_Scealy_Robust_Principal_Component_2015.pdf||553.89 kB||Adobe PDF||Request a copy|
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