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- Functional_principal_component_analysis abstract "Functional principal component analysis (FPCA) is a statistical method for investigating the dominant modes of variation of functional data. Using this method, a random function is represented in the eigenbasis, which is an orthonormal basis of the Hilbert space L2 that consists of the eigenfunctions of the autocovariance operator. FPCA represents functional data in the most parsimonious way, in the sense that when using a fixed number of basis functions, the eigenfunction basis explains more variation than any other basis expansion. FPCA can be applied for representing random functions, or functional regression and classification.".
- Functional_principal_component_analysis wikiPageID "41204236".
- Functional_principal_component_analysis wikiPageRevisionID "584618634".
- Functional_principal_component_analysis subject Category:Non-parametric_statistics.
- Functional_principal_component_analysis subject Category:Statistical_methods.
- Functional_principal_component_analysis comment "Functional principal component analysis (FPCA) is a statistical method for investigating the dominant modes of variation of functional data. Using this method, a random function is represented in the eigenbasis, which is an orthonormal basis of the Hilbert space L2 that consists of the eigenfunctions of the autocovariance operator.".
- Functional_principal_component_analysis label "Functional principal component analysis".
- Functional_principal_component_analysis sameAs m.0zdtcf_.
- Functional_principal_component_analysis sameAs Q17014987.
- Functional_principal_component_analysis sameAs Q17014987.
- Functional_principal_component_analysis wasDerivedFrom Functional_principal_component_analysis?oldid=584618634.
- Functional_principal_component_analysis isPrimaryTopicOf Functional_principal_component_analysis.