A generalization of the principal component analysis.
A generalization of the principal component analysis.
A nonlinear generalization of the principal component analysis (PCA) is made under normality. It is shown that this generalized PCA problem leads to an eigenvalue problem for the Hadamard products of the correlation matrix. In the framework of the generalized PCA, the result is applied to the problem of finding square-integrable continuous transformations.
Eigenvalues, singular values, and eigenvectors, normality, Hadamard products of the correlation matrix, principal component analysis, eigenvalue problem, Factor analysis and principal components; correspondence analysis, square- integrable continuous transformations, nonlinear generalization
Eigenvalues, singular values, and eigenvectors, normality, Hadamard products of the correlation matrix, principal component analysis, eigenvalue problem, Factor analysis and principal components; correspondence analysis, square- integrable continuous transformations, nonlinear generalization
selected citations These citations are derived from selected sources.This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).0 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
Citations provided by BIP!Popularity provided by BIP!