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- W1983791124 abstract "Block-structured correlation matrices are correlation matrices in which the p variables are subdivided into homogeneous groups, with equal correlations for variables within each group, and equal correlations between any given pair of variables from different groups. Block-structured correlation matrices arise as approximations for certain data sets’ true correlation matrices. A block structure in a correlation matrix entails a certain number of properties regarding its eigendecomposition and, therefore, a principal component analysis of the underlying data. This paper explores these properties, both from an algebraic and a geometric perspective, and discusses their robustness. Suggestions are also made regarding the choice of variables to be subjected to a principal component analysis, when in the presence of (approximately) block-structured variables." @default.
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- W1983791124 date "2010-03-03" @default.
- W1983791124 modified "2023-09-27" @default.
- W1983791124 title "The eigenstructure of block-structured correlation matrices and its implications for principal component analysis" @default.
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- W1983791124 doi "https://doi.org/10.1080/02664760902803263" @default.
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