What is another word for PCA?
Pronunciation: [pˌiːsˌiːˈe͡ɪ] (IPA) 
PCA, or principal component analysis, is a statistical procedure used to reduce the complexity of large data sets by identifying the most important variables. Synonyms for PCA include dimension reduction, feature extraction, variable reduction, and factor analysis. Dimension reduction is the process of reducing the number of variables in a data set, while feature extraction involves identifying the most important features. Variable reduction is similar to dimension reduction, while factor analysis involves identifying latent factors that underlie relationships between variables. Other synonyms for PCA include principal axis factoring, eigenvalue decomposition, and singular value decomposition. Regardless of the synonym used, PCA is a valuable tool for data analysis and can help uncover important insights from large data sets.
