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.
Learn about feature selection algorithms, such as sequential feature selection.
Neighborhood component analysis (NCA) is a non-parametric and embedded method for selecting features with the goal of maximizing prediction accuracy of regression and classification algorithms.
Feature extraction is a set of methods to extract high-level features from data.
This example shows a complete workflow for feature extraction from image data.
This example shows how to use
disentangle mixed audio signals.
t-SNE is a method for visualizing high-dimensional data by nonlinear reduction to two or three dimensions, while preserving some features of the original data.
This example shows how t-SNE creates a useful low-dimensional embedding of high-dimensional data.
This example shows the effects of various
Output function description and example for t-SNE.
Principal Component Analysis reduces the dimensionality of data by replacing several correlated variables with a new set of variables that are linear combinations of the original variables.
Perform a weighted principal components analysis and interpret the results.
Factor analysis is a way to fit a model to multivariate data to estimate interdependence of measured variables on a smaller number of unobserved (latent) factors.
Use factor analysis to investigate whether companies within the same sector experience similar week-to-week changes in stock prices.
This example shows how to perform factor analysis using Statistics and Machine Learning Toolbox™.
Nonnegative matrix factorization (NMF) is a dimension-reduction technique based on a low-rank approximation of the feature space.
Perform nonnegative matrix factorization using the multiplicative and alternating least-squares algorithms.
Multidimensional scaling allows you to visualize how near points are to each other for many kinds of distance or dissimilarity metrics and can produce a representation of data in a small number of dimensions.
perform classical (metric) multidimensional scaling, also known as
principal coordinates analysis.
This example shows how to perform classical multidimensional scaling using the
cmdscale function in Statistics and Machine Learning Toolbox™.
This example shows how to visualize dissimilarity data using nonclassical forms of multidimensional scaling (MDS).
Perform nonclassical multidimensional scaling using
Procrustes analysis minimizes the differences in location between compared landmark data using the best shape-preserving Euclidean transformations
Use Procrustes analysis to compare two handwritten numerals.