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

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

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

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

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

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). PCR is a form...

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

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

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...

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

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

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

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...

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

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

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...

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

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

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

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...

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

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

semantic analysis Latent semantic mapping Latent semantic structure indexing Principal components analysis Probabilistic latent semantic analysis Spamdexing...

(Principal component analysis in the time domain), on the other. Thus, SSA can be used as a time-and-frequency domain method for time series analysis —...

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

transform (via discrete Fourier transform) analysis that filters-off physiological signals. Principal component analysis of digital holograms reconstructed from...

algorithms like k-means, dimensionality reduction techniques like principal component analysis (PCA), Boltzmann machine learning, and autoencoders. After the...

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...