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

Componential analysis (feature analysis or contrast analysis) is the analysis of words through structured sets of semantic features, which are given as...

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

subcomponents Neighbourhood components analysis, an unsupervised learning method for classification multivariate data Componential analysis This disambiguation...

which analyzes how people classify and label their world, and componential analysis, which dissects semantic features of terms to understand cultural...

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

Component analysis is the analysis of two or more independent variables which comprise a treatment modality. It is also known as a dismantling study. The...

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

Dependent component analysis (DCA) is a blind signal separation (BSS) method and an extension of Independent component analysis (ICA). ICA is the separating...

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

component. Additionally, semantic features/semantic components are also often referred to as semantic properties. The theory of componential analysis...

Factor analysis is a statistical method used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved...

Neighbourhood components analysis is a supervised learning method for classifying multivariate data into distinct classes according to a given distance...

ANOVA–simultaneous component analysis (ASCA or ANOVA-SCA) is a statistical technique used to analyze complex datasets, particularly those arising from...

(1931-1960s), relational semantics (from the 1960s by John Lyons) and componential analysis (from the 1960s by Eugenio Coseriu, Bernard Pottier and Algirdas...

describe words multilingually. Such elements provide a bridge to componential analysis and the initial work of ontologies. Asemic writing Meme Phoneme...

Connected-component labeling (CCL), connected-component analysis (CCA), blob extraction, region labeling, blob discovery, or region extraction is an algorithmic...

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

the components of a particular chemical compound (qualitative analysis), to identify the proportions of components in a mixture (quantitative analysis),...

interconnected components. Network analysis is the process of finding the voltages across, and the currents through, all network components. There are many...

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

Linear discriminant analysis (LDA), normal discriminant analysis (NDA), canonical variates analysis (CVA), or discriminant function analysis is a generalization...

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

considered at a particular level of analysis Lumped element model, a model of spatially distributed systems Component video, a type of analog video information...

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

kernel-independent component analysis (kernel ICA) is an efficient algorithm for independent component analysis which estimates source components by optimizing...

subdivision of statistics encompassing the simultaneous observation and analysis of more than one outcome variable, i.e., multivariate random variables...

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...

"Yankee Kinship Terminology: A Problem in Componential Analysis." In E.A. Hammel, ed., Formal Semantic Analysis, pp259–297. Special Publication, American...

equivalence." Nida also developed the componential analysis technique, which split words into their components to help determine equivalence in translation...