Nearest Neighbor Based Feature Selection for Regression and Its Application to Neural Activity
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 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.
Cite
Text
Navot et al. "Nearest Neighbor Based Feature Selection for Regression and Its Application to Neural Activity." Neural Information Processing Systems, 2005.Markdown
[Navot et al. "Nearest Neighbor Based Feature Selection for Regression and Its Application to Neural Activity." Neural Information Processing Systems, 2005.](https://mlanthology.org/neurips/2005/navot2005neurips-nearest/)BibTeX
@inproceedings{navot2005neurips-nearest,
title = {{Nearest Neighbor Based Feature Selection for Regression and Its Application to Neural Activity}},
author = {Navot, Amir and Shpigelman, Lavi and Tishby, Naftali and Vaadia, Eilon},
booktitle = {Neural Information Processing Systems},
year = {2005},
pages = {996-1002},
url = {https://mlanthology.org/neurips/2005/navot2005neurips-nearest/}
}