Non-linear dimensionality reduction: Riemannian metric estimation and the problem of geometric discovery

30 May 2013Dominique Perraul-JoncasMarina Meila

In recent years, manifold learning has become increasingly popular as a tool for performing non-linear dimensionality reduction. This has led to the development of numerous algorithms of varying degrees of complexity that aim to recover man ifold geometry using either local or global features of the data... (read more)

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