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...

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

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

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

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

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

fewer dimensions. The data transformation may be linear, as in principal component analysis (PCA), but many nonlinear dimensionality reduction techniques...

and principal component analysis. High dimensional data can be hard for machines to work with, requiring significant time and space for analysis. It also...

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

correspond to principal components and the eigenvalues to the variance explained by the principal components. Principal component analysis of the correlation...

counterpart of principal component analysis for categorical data.[citation needed] MCA can be viewed as an extension of simple correspondence analysis (CA) in...

smaller reconstruction error compared to the first 30 components of a principal component analysis (PCA), and learned a representation that was qualitatively...

as principal component analysis (PCA), independent component analysis (ICA), linear discriminant analysis (LDA) and canonical correlation analysis (CCA)...

Sparse principal component analysis (SPCA or sparse PCA) is a technique used in statistical analysis and, in particular, in the analysis of multivariate...

geometric data analysis and statistical shape analysis, principal geodesic analysis is a generalization of principal component analysis to a non-Euclidean...

European Turkey) around 7000 BC. At the autosomal level, in the Principal component analysis (PCA) the analyzed AHG individual turns out to be close to two...

variables, such as by factor analysis, regression analysis, or principal component analysis Principal component analysis – transformation of a sample...

network for unsupervised learning with applications primarily in principal components analysis. First defined in 1989, it is similar to Oja's rule in its formulation...

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...

Mehdi Pirooznia, and Eran Elhaik in Frontiers in Genetics, in a principal component analysis, Natufians, together with a Neolithic Levantine sample, "clustered...

; Sastry, S.S. (2005). "Generalized principal component analysis (GPCA)". IEEE Transactions on Pattern Analysis and Machine Intelligence. 27 (12): 1945–1959...

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

measures such as a principal component analysis, GPA uses individual level data and a measure of variance is utilized in the analysis. The Procrustes distance...

decomposition and principal component analysis of the surprisal was utilized to identify constraints on biological systems, extending surprisal analysis to better...

Directional component analysis (DCA) is a statistical method used in climate science for identifying representative patterns of variability in space-time...

using this method. Dimensionality reduction algorithms such as Principal component analysis (PCA) and t-SNE can be used to simplify data for visualisation...