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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Bibliographic Details
Published inIEEE transactions on geoscience and remote sensing Vol. 45; no. 3; pp. 756 - 764
Main Authors KUO, Bor-Chen, CHANG, Kuang-Yu
Format Journal Article
LanguageEnglish
Published New York, NY IEEE 01.03.2007
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
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
algorithms
eigenvalues
discriminant analysis
small sample size classification
feature extraction
regularization
Eigenvalue decomposition
accuracy
genetic algorithm (GA)
classification
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Summary: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
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2006.885074