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...

multivariate statistics, kernel principal component analysis (kernel PCA) is an extension of principal component analysis (PCA) using techniques of kernel...

Robust Principal Component Analysis (RPCA) is a modification of the widely used statistical procedure of principal component analysis (PCA) which works...

In statistics, principal component regression (PCR) is a regression analysis technique that is based on principal component analysis (PCA). More specifically...

Multilinear principal component analysis (MPCA) is a multilinear extension of principal component analysis (PCA) that is used to analyze M-way arrays,...

Component analysis may refer to one of several topics in statistics: Principal component analysis, a technique that converts a set of observations of...

principal component analysis (L1-PCA) is a general method for multivariate data analysis. L1-PCA is often preferred over standard L2-norm principal component...

In signal processing, independent component analysis (ICA) is a computational method for separating a multivariate signal into additive subcomponents....

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