文献の詳細
| 言語 | 英語 |
|---|---|
| 著者 | Shin'ichiro Omachi, Fang Sun, and Hirotomo Aso |
| 論文名 | A New Approximation Method of the Quadratic Discriminant Function |
| 論文誌名 | Lecture Notes in Computer Science (Joint IAPR International Workshops SSPR 2000 and SPR 2000) |
| Vol. | 1876 |
| ページ | pp.601-610 |
| 年月 | 2000年9月 |
| 要約 | For many statistical pattern recognition methods, distributions of sample vectors are assumed to be normal, and the quadratic discriminant function derived from the probability density function of multivariate normal distribution is used for classification. However, the computational cost is $O(n^2)$ for $n$-dimensional vectors. Moreover, if there are not enough training sample patterns, covariance matrix can not be estimated accurately. In the case that the dimensionality is large, these disadvantages markedly reduce classification performance. In order to avoid these problems, in this paper, a new approximation method of the quadratic discriminant function is proposed. This approximation is done by replacing the values of small eigenvalues by a constant which is estimated by the maximum likelihood estimation. This approximation not only reduces the computational cost but also improves the classification accuracy. |
@Article {Omachi2000,
author = {Shin'ichiro Omachi and Fang Sun and Hirotomo Aso},
title = {A New Approximation Method of the Quadratic Discriminant Function},
journal = {Lecture Notes in Computer Science (Joint IAPR International Workshops SSPR 2000 and SPR 2000)},
year = {2000},
month = sep,
volume = {1876},
pages = {601--610}
}