Add diffusion map dimensionality reduction and simple Principal Component Analysis #17
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Spectral Clustering consists of two steps. First a dimensionality reduction finds important pattern in a graph between the points (by projecting the graph), then a clustering algorithm runs on the embedding. The technique is non-linear because cluster can be distributed such that they are nested inside each other, for example swiss-rolls or nested rings. A linear projection (like PCA) fails in such cases.
Gaussian and polynomial kernels are implemented and projected with diffusion maps.
LOBPCG
is used for finding thek
largest eigenvalues.Things todo before merging: