![]() Finding such new variables, the principal components (PCs), reduces to solving an eigenvalue/eigenvector problem. This means that ‘preserving as much variability as possible’ translates into finding new variables that are linear functions of those in the original dataset, that successively maximize variance and that are uncorrelated with each other. statistical information) as possible.Īlthough it is used, and has sometimes been reinvented, in many different disciplines it is, at heart, a statistical technique and hence much of its development has been by statisticians. Its idea is simple-reduce the dimensionality of a dataset, while preserving as much ‘variability’ (i.e. Many techniques have been developed for this purpose, but principal component analysis (PCA) is one of the oldest and most widely used. In order to interpret such datasets, methods are required to drastically reduce their dimensionality in an interpretable way, such that most of the information in the data is preserved. ![]() Large datasets are increasingly widespread in many disciplines.
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