Results 231 to 240 of about 3,902,266 (287)

Feature Extractions for Small Sample Size Classification Problem

IEEE Transactions on Geoscience and Remote Sensing, 2007
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 ...
Bor-Chen Kuo, Kuang-Yu Chang
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Small Sample Power Curves for the Two Sample Location Problem

Technometrics, 1969
The two major assumptions required by the two-sample t test to guarantee α are normality and a known ratio (usually 1) of variances in the two populations. Alternatives to this test are reviewed for situations where either or both of these assumptions are in doubt.
Paul Leaverton, John J. Birch
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Saddlepoint approximations for small sample logistic regression problems

Statistics in Medicine, 2000
Double saddlepoint approximations provide quick and accurate approximations to exact conditional tail probabilities in a variety of situations. This paper describes the use of these approximations in two logistic regression problems. An investigation of regression analysis of the log-odds ratio in a sequence or set of 2x2 tables via simulation studies ...
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Solving the small sample size problem of LDA

Object recognition supported by user interaction for service robots, 2003
The small sample size problem is often encountered in pattern recognition. It results in the singularity of the within-class scattering matrix S/sub w/ in linear discriminant analysis (LDA). Different methods have been proposed to solve this problem in face recognition literature. Some methods reduce the dimension of the original sample space and hence
null Rui Huang, null Qingshan Liu, null Hanqing Lu, null Songde Ma   +3 more
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Too Small to Succeed: Small Samples and the p-Value Problem

ACM SIGMIS Database: the DATABASE for Advances in Information Systems
Determining an appropriate sample size is a critical planning decision in quantitative empirical research. In recent years, there has been a growing concern that researchers have excessively focused on statistical significance in large sample studies to the detriment of effect sizes.
Miguel I. Aguirre-Urreta, Mikko Rönkkö, Cameron N. McIntosh   +2 more
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Small-Sample Statistical Estimates for the Sensitivity of Eigenvalue Problems

SIAM Journal on Matrix Analysis and Applications, 1997
To estimate the sensitivity of eigenvalue computations based on equal amount of perturbations in the matrix elements is not appropriate in a variety of practical applications. In such cases, it is pointed out that a statistical approach to the perturbations is better.
Gudmundsson, Thorkell, Kenney, Charles, Laub, Alan J.   +2 more
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INFORMATION DIFFUSION TECHNIQUES AND SMALL-SAMPLE PROBLEM

International Journal of Information Technology & Decision Making, 2002
Strong interests in the small-sample problem have been given towards for establishing several information diffusion techniques for pattern recognition. In this paper, we review and formalize three techniques: the soft histogram, the self-study discrete regression, and the interior-outer-set model. To promote the development of this area, in this paper
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Exponential locality preserving projections for small sample size problem

Neurocomputing, 2011
Locality preserving projections (LPP) is a widely used manifold reduced dimensionality technique. However, it suffers from two problems: (1) small sample size problem and (2) the performance is sensitive to the neighborhood size k. In order to address these problems, we propose an exponential locality preserving projections (ELPP) by introducing the ...
Su-Jing Wang, Hui-Ling Chen, Xu-Jun Peng, Chun-Guang Zhou   +3 more
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Kernel quadratic discriminant analysis for small sample size problem

Pattern Recognition, 2008
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Wang, Jie, Plataniotis, K. N., Lu, Juwei, Venetsanopoulos, A. N.   +3 more
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Solutions of LDA for Small Sample Size Problems

2006
This chapter shows the solutions of LDA for small sample-size (SSS) problems. We first give an overview on the existing LDA regularization techniques. Then, a unified framework for LDA and a combined LDA algorithm for SSS problem are described. Finally, we provide the experimental results and some conclusions.
David Zhang, Xiao-Yuan Jing, Jian Yang
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