DMCA
A Direct Evolutionary Feature Extraction Algorithm for Classifying (2006)
| Venue: | High Dimensional Data,” American Association for Artificial Intelligence 2006 |
| Citations: | 2 - 2 self |
BibTeX
@INPROCEEDINGS{Zhao06adirect,
author = {Qijun Zhao and David Zhang and Hongtao Lu},
title = {A Direct Evolutionary Feature Extraction Algorithm for Classifying},
booktitle = {High Dimensional Data,” American Association for Artificial Intelligence 2006},
year = {2006},
pages = {561--566}
}
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Abstract
Among various feature extraction algorithms, those based on genetic algorithms are promising owing to their potential parallelizability and possible applications in large scale and high dimensional data classification. However, existing genetic algorithm based feature ex-traction algorithms are either limited in searching opti-mal projection basis vectors or costly in both time and space complexities and thus not directly applicable to high dimensional data. In this paper, a direct evolution-ary feature extraction algorithm is proposed for classify-ing high-dimensional data. It constructs projection basis vectors using the linear combination of the basis of the search space and the technique of orthogonal comple-ment. It also constrains the search space when seeking for the optimal projection basis vectors. It evaluates in-dividuals according to the classification performance on a subset of the training samples and the generalization ability of the projection basis vectors represented by the individuals. We compared the proposed algorithm with some representative feature extraction algorithms in face recognition, including the evolutionary pursuit algorithm, Eigenfaces, and Fisherfaces. The results on the widely-used Yale and ORL face databases show that the proposed algorithm has an excellent performance in classification while reducing the space complexity by an order of magnitude. I.
Keyphrases
direct evolutionary feature extraction algorithm genetic algorithm space complexity search space projection basis vector possible application evolutionary pursuit algorithm orthogonal comple-ment various feature extraction algorithm representative feature extraction algorithm linear combination optimal projection basis vector classify-ing high-dimensional data high dimensional data classification feature ex-traction algorithm face recognition large scale generalization ability excellent performance opti-mal projection basis vector high dimensional data widely-used yale potential parallelizability direct evolution-ary feature extraction algorithm classification performance training sample
