Weighted principal component extraction with genetic algorithms

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...

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Main Authors: Liu, Nan, Wang, Han
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2013
Online Access:https://hdl.handle.net/10356/98883
http://hdl.handle.net/10220/12638
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-988832020-03-07T14:00:28Z Weighted principal component extraction with genetic algorithms Liu, Nan Wang, Han School of Electrical and Electronic Engineering 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. 2013-07-31T07:02:54Z 2019-12-06T20:00:47Z 2013-07-31T07:02:54Z 2019-12-06T20:00:47Z 2012 2012 Journal Article Liu, N.,& Wang, H. (2012). Weighted principal component extraction with genetic algorithms. Applied Soft Computing, 12(2), 961-974. 1568-4946 https://hdl.handle.net/10356/98883 http://hdl.handle.net/10220/12638 10.1016/j.asoc.2011.08.030 en Applied soft computing
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
description 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.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Liu, Nan
Wang, Han
format Article
author Liu, Nan
Wang, Han
spellingShingle Liu, Nan
Wang, Han
Weighted principal component extraction with genetic algorithms
author_sort Liu, Nan
title Weighted principal component extraction with genetic algorithms
title_short Weighted principal component extraction with genetic algorithms
title_full Weighted principal component extraction with genetic algorithms
title_fullStr Weighted principal component extraction with genetic algorithms
title_full_unstemmed Weighted principal component extraction with genetic algorithms
title_sort weighted principal component extraction with genetic algorithms
publishDate 2013
url https://hdl.handle.net/10356/98883
http://hdl.handle.net/10220/12638
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