Weighted SOM-Face: selecting local features for recognition from individual face image
IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning
Feature selection for high dimensional face image using self-organizing maps
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
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Feature selection is an important consideration in several applications where one needs to choose a smaller subset of features from a complete set of raw measurements such that the improved subset generates as good or better classification performance compared to original data. In this paper, we describe a novel feature selection approach that is based on the estimation of classification complexity though data partitioning. This approach allows us to select the N best features from a given set in order of their ability to separate data from different classes. In this paper, we perform our experiments on the ORLface database thatconsists of 400 images. The results show that the proposed approach outperforms the probability distance approach and is a viable method for implementing more advanced search methods of feature selection.