PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Nearest Neighbor Based Feature Selection for Regression and its Application to Neural Activity
Amir Navot, Lavi Shpigelman, Naftali Tishby and Eilon Vaadia
Advances in Neural Information Processing Systems (NIPS) Volume 19, 2005.


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.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Brain Computer Interfaces
Theory & Algorithms
ID Code:2005
Deposited By:Naftali Tishby
Deposited On:14 January 2006