Feature projection (also called feature extraction) transforms the data from the high-dimensional space to a space of fewer dimensions. The data transformation may be linear, as in principal component analysis (PCA), but many nonlinear dimensionality reduction techniques also exist, for example multidimensional scaling (MDS), t-distributed stochastic neighbor embedding (t-SNE), etc.
