Reference : Supervised dimension reduction of intrinsically low-dimensional data
Scientific journals : Article
Engineering, computing & technology : Computer science
Supervised dimension reduction of intrinsically low-dimensional data
Vlassis, Nikos mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Motomura, Y. [> >]
Kröse, B. [> >]
Neural Computation
MIT Press
191 - 215
Yes (verified by ORBilu)
United Kingdom
[en] High-dimensional data generated by a system with limited degrees of freedom are often constrained in low-dimensional manifolds in the original space. In this article, we investigate dimension-reduction methods for such intrinsically low-dimensional data through linear projections that preserve the manifold structure of the data. For intrinsically one-dimensional data, this implies projecting to a curve on the plane with as few intersections as possible. We are proposing a supervised projection pursuit method that can be regarded as an extension of the single-index model for nonparametric regression. We show results from a toy and two robotic applications.

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