Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/98883
Title: Weighted principal component extraction with genetic algorithms
Authors: Liu, Nan
Wang, Han
Issue Date: 2012
Source: Liu, N.,& Wang, H. (2012). Weighted principal component extraction with genetic algorithms. Applied Soft Computing, 12(2), 961-974.
Series/Report no.: Applied soft computing
Abstract: Pattern recognition techniques have been widely used in a variety of scientific disciplines including computer vision, artificial intelligence, biology, and so forth. Although many methods present satisfactory performances, they still have several weak points, thus leaving a lot of space for further improvements. In this paper, we propose two performance-driven subspace learning methods by extending the principal component analysis (PCA) and the kernel PCA (KPCA). Both methods adopt a common structure where genetic algorithms are employed to pursue optimal subspaces. Because the proposed feature extractors aim at achieving high classification accuracy, enhanced generalization ability can be expected. Extensive experiments are designed to evaluate the effectiveness of the proposed algorithms in real-world problems including object recognition and a number of machine learning tasks. Comparative studies with other state-of-the-art techniques show that the methods in this paper are capable of enhancing generalization ability for pattern recognition systems.
URI: https://hdl.handle.net/10356/98883
http://hdl.handle.net/10220/12638
ISSN: 1568-4946
DOI: http://dx.doi.org/10.1016/j.asoc.2011.08.030
metadata.item.grantfulltext: none
metadata.item.fulltext: No Fulltext
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