Dimensionality Reduction

PCA, factor analysis, nonnegative matrix factorization, sequential feature selection, and more

Feature transformation techniques reduce the dimensionality in the data by transforming data into new features. Feature selection techniques are preferable when transformation of variables is not possible, e.g., when there are categorical variables in the data. For a feature selection technique that is specifically suitable for least-squares fitting, see Stepwise Regression.


PCA and Canonical Correlation

barttest Bartlett's test
canoncorr Canonical correlation
pca Principal component analysis of raw data
pcacov Principal component analysis on covariance matrix
pcares Residuals from principal component analysis
ppca Probabilistic principal component analysis

Factor Analysis

factoran Factor analysis
rotatefactors Rotate factor loadings

Nonnegative Matrix Factorization

nnmf Nonnegative matrix factorization

Multidimensional Scaling

cmdscale Classical multidimensional scaling
mahal Mahalanobis distance
mdscale Nonclassical multidimensional scaling
pdist Pairwise distance between pairs of objects
squareform Format distance matrix

Procrustes Analysis

procrustes Procrustes analysis

Feature Selection

sequentialfs Sequential feature selection
relieff Importance of attributes (predictors) using ReliefF algorithm
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