Dirk Ormoneit, Trevor Hastie
Local linear regression performs very well in many low-dimensional forecasting problems. In high-dimensional spaces, its performance typically decays due to the well-known "curse-of-dimensionality". A possible way to approach this problem is by varying the "shape" of the weighting kernel. In this work we suggest a new, data-driven method to estimating the optimal kernel shape. Experiments us(cid:173) ing an artificially generated data set and data from the UC Irvine repository show the benefits of kernel shaping.