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

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

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

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

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

[c] box plots for skewed data,[f] and robust principal component analysis,[d] and for her implementations of robust statistical algorithms in the R statistical...

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

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

may refer to: Reformed Presbyterian Church of Australia Robust principal component analysis Research, Protection, Containment Authority This disambiguation...

Reward-based selection Richard Zemel Right to explanation RoboEarth Robust principal component analysis RuleML Symposium Rule induction Rules extraction system family...

of multivariate, regression and functional data, and on robust principal component analysis. His current research is on visualization of classification...

these plots Dimensionality reduction: Multidimensional scaling Principal component analysis (PCA) Multilinear PCA Nonlinear dimensionality reduction (NLDR)...

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...

electrical engineer known for her research in compressed sensing, robust principal component analysis, signal processing, statistical learning theory, and computer...

(See Robust principal component analysis for more details) Dynamic RPCA for background/foreground separation (See Robust principal component analysis for...

analysis of variance to data analysis was published in 1921, Studies in Crop Variation I. This divided the variation of a time series into components...

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

validation Robust regression Segmented regression Signal processing Stepwise regression Taxicab geometry Linear trend estimation Necessary Condition Analysis David...

models when neural networks implement a form of Principal Component Analysis or Independent Component Analysis. A "clustering" is essentially a set of such...

two-stage procedure first reduces the predictor variables using principal component analysis, and then uses the reduced variables in an OLS regression fit...

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

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

(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 —...

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

important components of a systematic review. The term "meta-analysis" was coined in 1976 by the statistician Gene Glass, who stated "Meta-analysis refers...

of nonparametric regression analysis", Soviet Automatic Control, 12 (5): 25–34 William S. Cleveland (December 1979). "Robust Locally Weighted Regression...

(algorithm). Essentially the methods represent the application of a principal components analysis (PCA) approach to ensembles of observed time-series obtained...

Robust statistics are statistics that maintain their properties even if the underlying distributional assumptions are incorrect. Robust statistical methods...

In robust statistics, robust regression seeks to overcome some limitations of traditional regression analysis. A regression analysis models the relationship...

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...