Locally Weighted Projection Regression (LWPR)
is a new algorithm that achieves nonlinear function approximation
in high dimensional spaces with redundant and irrelevant
input dimensions. At its core, it uses locally linear models
, spanned by a small number of univariate regressions in selected directions
in input space. A locally weighted variant of Partial Least Squares (PLS)
is employed for doing the dimensionality reduction. This nonparametric
local learning system i) learns rapidly with second order learning methods
based on incremental training, ii) uses statistically sound stochastic cross
validation to learn iii) adjusts its weighting kernels based on local information
only, iv) has a computational complexity that is linear in the number of inputs,
and v) can deal with a large number of  possibly redundant  inputs, as
shown in evaluations with up to 50 dimensional data sets. To our knowledge,
this is the first truly incremental spatially localized learning method to
combine all these properties.
[Thanks to:
Stefan Schaal, Marc Toussaint, Giorgos Petkos, Narayanan Edakunni for
the implementations]

LWPR Users Manual (28 pages)


LWPR Other References  References (pdf) 
LWPR Matlab Code  LWPR test 
LWPR C++ Code (Beta) gzip

LWPR_C++ 