Principal component analysis (PCA) is a linear dimensionality reduction technique with applications in exploratory data analysis, visualization and data...

Functional principal component analysis (FPCA) is a statistical method for investigating the dominant modes of variation of functional data. Using this...

term is also interchangeable with the geographically weighted Principal components analysis in geophysics. The i th basis function is chosen to be orthogonal...

as the Karhunen-Loève decomposition. A rigorous analysis of functional principal components analysis was done in the 1970s by Kleffe, Dauxois and Pousse...

Spatial Principal Component Analysis (sPCA) is a multivariate statistical technique that complements the traditional Principal Component Analysis (PCA)...

show the contribution of each eigenfunction to the mean. Functional principal component analysis (FPCA) can be directly applied to the probability density...

predictor to a finite number of regression coefficients. Functional principal component analysis (FPCA) that employs the Karhunen–Loève expansion is a common...

sequence of their functional principal component scores (FPCs) and eigenfunctions. In the FAM the responses (scalar or functional) conditional on the...

variation around the mean. Both in principal component analysis (PCA) and in functional principal component analysis (FPCA), modes of variation play an...

Functional regression is a version of regression analysis when responses or covariates include functional data. Functional regression models can be classified...

Folding@home Formal concept analysis Forward algorithm Fowlkes–Mallows index Frederick Jelinek Frrole Functional principal component analysis GATTO GLIMMER Gary...

(2009). "Principal component analysis vs. exploratory factor analysis" (PDF). SUGI 30 Proceedings. Retrieved 5 April 2012. SAS Statistics. "Principal Components...

argued that it is inherently functional. His early papers on the grammar of English make reference to the "functional components" of language, as "generalized...

defined as, Functional data analysis Functional principal component analysis Karhunen–Loève theorem Functional regression Generalized functional linear model...

principal axis theorem is a generalization of the method of completing the square from elementary algebra. In linear algebra and functional analysis,...

the LDA method. LDA is also closely related to principal component analysis (PCA) and factor analysis in that they both look for linear combinations of...

characteristics. The structural-functional approach is based on the view that a political system is made up of several key components, including interest groups...

integers. Examples of analysis without a metric include measure theory (which describes size rather than distance) and functional analysis (which studies topological...

"Tracking the New Zealand English NEAR/SQUARE merger using functional principal components analysis" (PDF). September 15–19, 2019. Wells 1982, p. 609. Wells...

calculation; population stratification, such as a Principal component analysis; association analysis such as genome-wide association study for both basic...

crucial to analysis of functional genomics data. Examples of techniques in this class are data clustering or principal component analysis for unsupervised...

connectivity is a recent expansion on traditional functional connectivity analysis which typically assumes that functional networks are static in time. DFC is related...

(PLS) regression is a statistical method that bears some relation to principal components regression and is a reduced rank regression; instead of finding hyperplanes...

factors or principal components in an analysis. The scree plot is used to determine the number of factors to retain in an exploratory factor analysis (FA) or...

The main methods for analysis of pump–probe data are multi-exponential fitting, principal component analysis, and phasor analysis. In multi-exponential...

debated and not consistently true across scientific fields. Principal components analysis (PCA) creates a new set of orthogonal variables that contain...

Specifically, these statistical methodologies include: principal component analysis canonical correlation analysis multivariate regression statistical classification...

The functional database model is used to support analytics applications such as financial planning and performance management. The functional database...

obtained from an arbitrary basis using the Gram–Schmidt process. In functional analysis, the concept of an orthonormal basis can be generalized to arbitrary...

Multilinear principal component analysis (MPCA) Nearest neighbor search Non-linear iterative partial least squares Polar decomposition Principal component analysis...