title: Supervised dimension reduction of intrinsically low-dimensional data.
abstract: 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.
authors.affiliation: RWCP, Autonomous Learning Functions SNN, University of Amsterdam, The Netherlands. vlassis@science.uva.nl
authors.forename: Nikos
authors.initials: N
authors.lastname: Vlassis
authors.forename: Yoichi
authors.initials: Y
authors.lastname: Motomura
authors.forename: Ben
authors.initials: B
authors.lastname: Kröse