Use cmdscale to perform classical (metric) multidimensional scaling, also known as principal coordinates analysis.
Analyze if companies within the same sector experience similar week-to-week changes in stock price.
Use Procrustes analysis to compare two handwritten number threes. Visually and analytically explore the effects of forcing size and reflection changes.
Visualize dissimilarity data using nonclassical 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
Perform classical multidimensional scaling using the cmdscale function in Statistics and Machine Learning Toolbox™. Classical multidimensional scaling, also known as Principal
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
Perform factor analysis using Statistics and Machine Learning Toolbox™.
Tune the regularization parameter in fscnca using cross-validation. Tuning the regularization parameter helps to correctly detect the relevant features in the data.
Perform feature selection that is robust to outliers using a custom robust loss function in NCA.
Visualize the MNIST data, which consists of images of handwritten digits, using the tsne function. The images are 28-by-28 pixels in grayscale. Each image has an associated label from 0