Name
Title | ||
|---|---|---|
Nearest Neighbor Based Feature Selection for Regression and its Application to Neural Activity |
Abstract | ||
|---|---|---|
We present a non-linear, simple, yet effective, feature sub set 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 algo rithm 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. |
Year | Venue | Keywords |
|---|---|---|
2005 | NIPS | nearest neighbor,feature selection,k nearest neighbor |
Field | DocType | Citations |
k-nearest neighbors algorithm,Regression,Feature selection,Pattern recognition,Best bin first,Computer science,Neural activity,Regularization (mathematics),Nearest-neighbor chain algorithm,Artificial intelligence,Machine learning | Conference | 28 |
PageRank | References | Authors |
3.11 | 6 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amir Navot | 1 | 321 | 20.04 |
| Shpigelman, Lavi | 2 | 68 | 7.09 |
| Naftali Tishby | 3 | 4186 | 894.35 |
| Vaadia, Eilon | 4 | 141 | 15.90 |