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Manifold Learning [t-SNE, LLE, Isomap, +] Made Easy

The Heart of Dimensionality Reduction

Principal Component Analysis is a powerful method, but it often fails in that it assumes that the data can be modelled linearly. PCA expressed new features as linear combinations of existing ones by multiplying each by a coefficient. To address the limitations of PCA, various techniques have been created by apply…