Feature Extractions for Small Sample Size Classification Problem
Much research has shown that the definitions of within-class and between-class scatter matrices and regularization technique are the key components to design a feature extraction for small sample size problems. In this paper, we illustrate the importance of another key component, eigenvalue decompos...
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| Published in | IEEE transactions on geoscience and remote sensing Vol. 45; no. 3; pp. 756 - 764 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
New York, NY
IEEE
01.03.2007
Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
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| Abstract | Much research has shown that the definitions of within-class and between-class scatter matrices and regularization technique are the key components to design a feature extraction for small sample size problems. In this paper, we illustrate the importance of another key component, eigenvalue decomposition method, and a new regularization technique was proposed. In the hyperspectral image experiment, the effects of these three components of feature extraction are explored on ill-posed and poorly posed conditions. The experimental results show that different regularization methods need to cooperate with different eigenvalue decomposition methods to reach the best performance, the proposed regularization method, regularized feature extraction (RFE) outperform others, and the best feature extraction for a small sample size classification problem is RFE with nonparametric weighted scatter matrices |
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| AbstractList | Much research has shown that the definitions of within-class and between-class scatter matrices and regularization technique are the key components to design a feature extraction for small sample size problems. In this paper, we illustrate the importance of another key component, eigenvalue decomposition method, and a new regularization technique was proposed. In the hyperspectral image experiment, the effects of these three components of feature extraction are explored on ill-posed and poorly posed conditions. The experimental results show that different regularization methods need to cooperate with different eigenvalue decomposition methods to reach the best performance, the proposed regularization method, regularized feature extraction (RFE) outperform others, and the best feature extraction for a small sample size classification problem is RFE with nonparametric weighted scatter matrices |
| Author | Kuang-Yu Chang Bor-Chen Kuo |
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| Keywords | algorithms eigenvalues discriminant analysis small sample size classification feature extraction regularization Eigenvalue decomposition accuracy genetic algorithm (GA) classification |
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| SubjectTerms | Applied geophysics Classification Covariance matrix Data preprocessing Decomposition Earth sciences Earth, ocean, space Eigenvalue decomposition Eigenvalues Eigenvalues and eigenfunctions Exact sciences and technology Feature extraction genetic algorithm (GA) Genetic algorithms Hyperspectral imaging Internal geophysics Linear discriminant analysis Mathematical analysis Matrices Matrix decomposition Regularization Scatter Scattering small sample size classification Statistics |
| Title | Feature Extractions for Small Sample Size Classification Problem |
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