Visualize dissimilarity data using non-classical forms of multidimensional scaling (MDS).
Use Principal Components Analysis (PCA) to fit a linear regression. PCA minimizes the perpendicular distances from the data to the fitted model. This is the linear case of what is known as
Visualize multivariate data using various statistical plots. Many statistical analyses involve only two variables: a predictor variable and a response variable. Such data are easy to
Perform "classical" multidimensional scaling, using the cmdscale function in the Statistics and Machine Learning Toolbox™. Classical multidimensional scaling, also known as
Select features for classifying high-dimensional data. More specifically, it shows how to perform sequential feature selection, which is one of the most popular feature selection