Principal manifold learning by sparse grids


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 non-linear problem is solved by a descent method which resembles the expectation maximization algorithm. We present our sparse grid principal manifold approach, discuss its properties and report on the results of numerical experiments for one-, two- and three-dimensional model problems.

DOI: 10.1007/s00607-009-0045-8

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@article{Feuersnger2009PrincipalML, title={Principal manifold learning by sparse grids}, author={Christian Feuers{\"a}nger and Michael Griebel}, journal={Computing}, year={2009}, volume={85}, pages={267-299} }