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...

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

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...

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

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

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

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

dimensionality reduction, such as singular value decomposition and principal component analysis. Consider a dataset represented as a matrix (or a database table)...

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

counterpart of principal component analysis for categorical data. MCA can be viewed as an extension of simple correspondence analysis (CA) in that it...

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

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)...

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

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

similar to principal component analysis, but applies to categorical rather than continuous data. In a similar manner to principal component analysis, it provides...

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

word representations (also known as neural word embeddings). Principal component analysis (PCA) is often used for dimension reduction. Given an unlabeled...

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

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

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...

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

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

analysis, also known as Horn's parallel analysis, is a statistical method used to determine the number of components to keep in a principal component...

and is closely related to principal component analysis (PCA) technique widely used in image processing and in data analysis in many fields. There exist...

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...

representation of face images. Sirovich and Kirby showed that principal component analysis could be used on a collection of face images to form a set of...