Nearest Neighbor Based Feature Selection for Regression and its Application to Neural Activity
Amir Navot, Lavi Shpigelman, Naftali Tishby and Eilon Vaadia
In: Advances in Neural Information Processing Systems (NIPS) 18, 12-14 Dec 2005, Vancouver, Canada.

Abstract

We present a non-linear, simple, yet effective, feature subset selection method for regression and use it in analyzing cortical neural activity. Our algorithm involves a feature-weighted version of the k-nearest-neighbor algorithm. It is able to capture complex dependency of the target function on its input and makes use of the leave -one-out error as a natural regularization. We explain the characteristics of our algorithm on synthetic problems and use it in the context of predicting hand velocity from spikes recorded in motor cortex of a behaving monkey. By applying feature selection we are able to improve prediction quality and suggest a novel way of exploring neural data.

Postscript - Requires a viewer, such as GhostView

[Edit]