Well-known linear transformation methods include principal component analysis, factor analysis, and projection pursuit.
Commonly applied methods include principal component analysis (PCA), various forms of cluster analysis (CA), and discriminant analysis (DA, both linear and quadratic), followed by more recent (neural network and fuzzy) methods [17], although the application of combined techniques has been reported in the literature [28].
The purpose of this article is to provide an overview of data reduction methods, including principal components analysis, factor analysis, reduced rank regression, and cluster analysis.
Villanueva took the microRNA data from the cancer genome atlas and used a series of statistical methods, including principal component analysis (PCA), to identify groups of tumors that share similar microRNA profiles.
As of today, there are many popular linear and non-linear dimensionality reduction methods including principal component analysis, kernel principal components analysis, Isomap, Laplacian Eigenmaps and local linear embedding.
Some common chemometric methods, including principal component analysis (PCA), linear discriminant analysis (LDA) and k-nearest neighbor (kNN) based hierarchical cluster analysis (HCA) were used to test the discriminatory power of the array.
Pattern recognition methods including principal components analysis (PCA), partial least-squared discriminant analysis (PLS-DA), orthogonal partial least-squared discriminant analysis (OPLS-DA) and computational system analysis were integrated to obtain comprehensive metabonomic profiling and pathways of the biological data sets.
Statistical methods, including principal component analysis (PCA), partial least-square regression (PLS) and discriminant analysis (PLS-DA), were applied to find metabolite patterns related to surgical trauma and postoperative hypoxemia.
Two multivariate methods including principal component analysis (PCA) and hierarchical clustering analysis (HCA) were utilized in this study.
Tsai and Chen [11] proposed using a MANOVA test for gene-set analysis and compared it to several methods including principal component analysis, SAM-GS, GSEA, MaxMean, analysis of covariance, and a global test.
Feature selection methods included principal component analysis (PCA), chisquare, gainratio, inforgain, relief, and SVM recursive feature election (SVM-RFE).
