Constrained Principal Components

Abstract

In the usual forms of least squares nonlinear principal component analysis observed variables are quantified or transformed to optimize low-rank approximations. Thus NLPCA is linear PCA on optimally scaled variables. In this note we extend the approach by allowing for optimally scaled components.

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