In 2008, several principal component analyses (PCAs) applied on Background. 660,918 single-nucleotide polymorphisms (SNPs) from 938 individuals from 51 worldwide populations of the Human Genome Diversity Panel were published by Li PCAs were applied on subsets of individuals sharing a common et al. geographic origin and showed that in several geographic regions, genome-wide variations of SNPs ...

In 2008, several principal component analyses (PCAs) applied on Background. 660,918 single-nucleotide polymorphisms (SNPs) from 938 individuals from 51 worldwide populations of the Human Genome Diversity Panel were published by Li PCAs were applied on subsets of individuals sharing a common et al. geographic origin and showed that in several geographic regions, genome-wide variations of SNPs ...

Exploratory Factor Analysis (EFA) and Principal Component Analysis (PCA) are popular techniques for simplifying presentation of, and investigating structure of, an (n×p) data matrix. However, these fundamentally different techniques are frequently confused, and the differences between them are obscured, because they give similar results in some practical cases. We therefore investigate conditio...

Principal Component Analysis (PCA) has been widely used for dimensionality reduction in shape and appearance modeling. There have been several attempts of making PCA robust against outliers. However, there are cases in which a small subset of samples may appear as outliers and still correspond to plausible data. The example of shapes corresponding to fractures when building a vertebra shape mod...