A study of regularized Gaussian classifier in high-dimension small sample set case based on MDL principle with application to spectrum recognition
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摘要In classifying high-dimensional patterns such as stellar spectra by a Gaussian classifier, the covariance matrix estimated with a small-number sample set becomes unstable, leading to degraded classification accuracy. In this paper, we investigate the covariance matrix estimation problem for small-number samples with high dimension setting based on minimum description length (MDL) principle. A new covariance matrix estimator is developed, and a formula for fast estimation of regularization parameters is derived. Experiments on spectrum pattern recognition are conducted to investigate the classification accuracy with the developed covariance matrix estimator. Higher classification accuracy results are obtained and demonstrated in our new approach. (c) 2008 Elsevier Ltd. All rights reserved.
著者Guo P, Jia YD, Lyu MR
期刊名稱Pattern Recognition
出版年份2008
月份9
日期1
卷號41
期次9
出版社Elsevier
頁次2842 - 2854
國際標準期刊號0031-3203
電子國際標準期刊號1873-5142
語言英式英語
關鍵詞classification; covariance matrix estimation; discriminant analysis method; minimum description length; regularization parameter selection
Web of Science 學科類別Computer Science; Computer Science, Artificial Intelligence; COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE; Engineering; Engineering, Electrical & Electronic; ENGINEERING, ELECTRICAL & ELECTRONIC

上次更新時間 2021-27-02 於 23:36