Principal Component Analyses in Anthropological Genetics
نویسندگان
چکیده
منابع مشابه
Replication of the principal component analyses of the human
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 ...
متن کاملReplication of the principal component analyses of the human
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 ...
متن کاملExploratory factor and principal component analyses: some new aspects
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...
متن کاملA Family of Principal Component Analyses for Dealing with Outliers
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...
متن کاملPrincipal Component Projection Without Principal Component Analysis
We show how to efficiently project a vector onto the top principal components of a matrix, without explicitly computing these components. Specifically, we introduce an iterative algorithm that provably computes the projection using few calls to any black-box routine for ridge regression. By avoiding explicit principal component analysis (PCA), our algorithm is the first with no runtime dependen...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Advances in Anthropology
سال: 2011
ISSN: 2163-9353,2163-9361
DOI: 10.4236/aa.2011.12002