期刊文献+

Analysis and Experiments on Two Linear Discriminant Analysis Methods

Analysis and Experiments on Two Linear Discriminant Analysis Methods
下载PDF
导出
摘要 Foley-Sammon linear discriminant analysis (FSLDA) and uncorrelated linear discriminant analysis (ULDA) are two well-known kinds of linear discriminant analysis. Both ULDA and FSLDA search the kth discriminant vector in an n-k+1 dimensional subspace, while they are subject to their respective constraints. Evidenced by strict demonstration, it is clear that in essence ULDA vectors are the covariance-orthogonal vectors of the corresponding eigen-equation. So, the algorithms for the covariance-orthogonal vectors are equivalent to the original algorithm of ULDA, which is time-consuming. Also, it is first revealed that the Fisher criterion value of each FSLDA vector must be not less than that of the corresponding ULDA vector by theory analysis. For a discriminant vector, the larger its Fisher criterion value is, the more powerful in discriminability it is. So, for FSLDA vectors, corresponding to larger Fisher criterion values is an advantage. On the other hand, in general any two feature components extracted by FSLDA vectors are statistically correlated with each other, which may make the discriminant vectors set at a disadvantageous position. In contrast to FSLDA vectors, any two feature components extracted by ULDA vectors are statistically uncorrelated with each other. Two experiments on CENPARMI handwritten numeral database and ORL database are performed. The experimental results are consistent with the theory analysis on Fisher criterion values of ULDA vectors and FSLDA vectors. The experiments also show that the equivalent algorithm of ULDA, presented in this paper, is much more efficient than the original algorithm of ULDA, as the theory analysis expects. Moreover, it appears that if there is high statistical correlation between feature components extracted by FSLDA vectors, FSLDA will not perform well, in spite of larger Fisher criterion value owned by every FSLDA vector. However, when the average correlation coefficient of feature components extracted by FSLDA vectors is at a low level, the performance of FSLDA are comparable with ULDA. Foley-Sammon linear discriminant analysis (FSLDA) and uncorrelated linear discriminant analysis (ULDA) are two well-known kinds of linear discriminant analysis. Both ULDA and FSLDA search the kth discriminant vector in an n - k + 1 dimensional subspace, while they are subject to their respective constraints. Evidenced by strict demonstration, it is clear that in essence ULDA vectors are the covarianceorthogonal vectors of the corresponding eigen-equation. So, the algorithms for the covariance-orthogonal vectors are equivalent to the original algorithm of ULDA, which is time-consuming. Also, it is first revealed that the Fisher criterion value of each FSLDA vector must be not less than that of the corresponding ULDA vector by theory analysis. For a discriminant vector, the larger its Fisher criterion value is, the more powerful in discriminability it is. So, for FSLDA vectors, corresponding to larger Fisher criterion values is an advantage. On the other hand, in general any two feature components extracted by FSLDA vectors are statistically correlated with each other, which may make the discriminant vectors set at a disadvantageous position. In contrast to FSLDA vectors, any two feature components extracted by ULDA vectors are statistically uncorrelated with each other. Two experiments on CENPARMI handwritten numeral database and ORL database are performed. The experimental results are consistent with the theory analysis on Fisher criterion values of ULDA vectors and FSLDA vectors. The experiments also show that the equivalent algorithm of ULDA, presented in this paper, is much more efficient than the original algorithm of ULDA, as the theory analysis expects. Moreover, it appears that if there is high statistical correlation between feature components extracted by FSLDA vectors, FSLDA will not perform well, in spite of larger Fisher criterion value owned by every FSLDA vector. However, when the average correlation coefficient of feature components extracted by FSLDA vectors is at a low level, the performance of FSLDA are comparable with ULDA.
出处 《工程科学(英文版)》 2006年第3期37-47,共11页 Engineering Sciences
基金 The National Natural Science Foundation of China (Grant No.60472060 ,60473039 and 60472061)
关键词 Fisher判据 Foley-Sammon线性判别分析 相关系数 不相关线性判别分析 判别向量 Fisher criterion Foley-Sammon linear discriminant analysis (FSLDA) uncorrelated linear discriminant analysis(ULDA) correlation coefficient
  • 相关文献

参考文献10

  • 1Duchene J,Leclercq S.An optimal transformation for discriminant and principal component analysis[].IEEE Trans Pattern Anal MachIntell.1988
  • 2Hamamoto Y,Kanaoka T,Tomita S.Orthogonal discriminant analysis for interactive pattern analysis [ A][].Proceedings Tenth International Conference on Pattern Recognition.1990
  • 3Hamamoto Y,Matsuura Y,Kanaoka T,Tomita S.Anote on the orthonormal discriminant vector method for feature extraction [ J ][].Pattern Recognition.1991
  • 4Kittler J.On the discriminant vector method of feature selection[].IEEE Trans Computation.1977
  • 5Ding Xueren,Cai Gaoting.Matrix Theory[]..1985
  • 6Cheng Yunpeng,Zhang Kaiyue,Xu Zhong.Matrix Theory[]..2002
  • 7Xu Yong,Yang Qiang,Yang Jingyu.Theory analysis on FSODVand an optimized model[].Chinese Journal of Computers.2003
  • 8Jin Zhong.Research on feature extraction of face images and feature dimensionality[]..1999
  • 9Xu Yong,Yang Jingyu,Lu Jianfeng.Discussion on Foley-Sammon optimal discriminant vector[].Third International Symposium on Multispectral Image Processing and Pattern Recognition.2003
  • 10Duda,R. O.,Hart,P. E. Pattern classification and scene analysis . 1973

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部