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Technical Program
Paper Detail
| Paper: | MP-P6.9 |
| Session: | Color and Multispectral Processing |
| Time: | Monday, October 9, 14:20 - 17:00 |
| Presentation: |
Poster
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| Title: |
FEATURE SELECTION USING A MIXED-NORM PENALTY FUNCTION |
| Authors: |
Huiwen Zeng; North Carolina State University | | | | | H. Joel Trussell; North Carolina State University | | |
| Abstract: |
Feature selection, is the process of selecting subsets of features that are effective in performing a given task. We propose an approach using a penalty function combined with a neural network to select a subset from a large collection of features while maintaining the performance possible with the larger set. The penalty function is related to a mixed-norm function that has proven successful in pruning neural networks. The new function is shown to work on test cases with known redundancy and to be effective in feature selection for practical problems. |
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