Functional principal component analysis
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 in functional regression and classification.
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- enFunctional 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 in functional regression and classification.
- Has abstract
- enFunctional 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 in functional regression and classification.
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- Functional principal component analysis
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- enFunctional principal component analysis
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- Basis functions
- Best linear unbiased prediction
- Category:Factor analysis
- Category:Nonparametric statistics
- Covariance operator
- Dimensionality reduction
- Factor analysis
- Functional data analysis
- Hilbert–Schmidt operator
- Hilbert space
- Interpolation
- Karhunen–Loève theorem
- Local regression
- Longitudinal data
- Modes of variation
- Numerical integration
- Orthonormality
- Permutation
- Positive-definite matrix
- Principal component analysis
- Random function
- Regularization (mathematics)
- Spline smoothing
- Square-integrable function
- Statistics
- Stochastic process
- Symmetric matrix
- SameAs
- fJRo
- m.0zdtcf
- Q17014987
- Subject
- Category:Factor analysis
- Category:Nonparametric statistics
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- Functional principal component analysis?oldid=1052178893&ns=0
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- 41204236
- Wikipage revision ID
- 1052178893
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