Part of Advances in Neural Information Processing Systems 18 (NIPS 2005)
Amir Navot, Lavi Shpigelman, Naftali Tishby, Eilon Vaadia
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 func- tion on its input and makes use of the leave-one-out error as a natural regularization. We explain the characteristics of our algorithm on syn- thetic problems and use it in the context of predicting hand velocity from spikes recorded in motor cortex of a behaving monkey. By applying fea- ture selection we are able to improve prediction quality and suggest a novel way of exploring neural data.