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Principal manifold learning by sparse grids
2009
Computing
In this paper we deal with the construction of lower-dimensional manifolds from high-dimensional data which is an important task in data mining, machine learning and statistics. Here, we consider principal manifolds as the minimum of a regularized, non-linear empirical quantization error functional. For the discretization we use a sparse grid method in latent parameter space. This approach avoids, to some extent, the curse of dimension of conventional grids like in the GTM approach. The arising
doi:10.1007/s00607-009-0045-8
fatcat:xewaqii2t5cjzclxmczpl37jey