Comparison of PCA and Manifold Learning -- astroML 0.2 documentation

The top-left panel shows an example S-shaped data set (a two-dimensional manifold in a three-dimensional space). PCA identifies three principal components within the data. Manifold learning (LLE and IsoMap) preserves the local structure when projecting the data, preventing the mixing of the colors.