Feature extraction based on the Bhattacharyya distance
Feature extraction based on the Bhattacharyya distance
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DOI：
10.1109/igarss.2000.858336
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发表时间：
2000-07
期刊：
影响因子：
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通讯作者：
E. Choi;Chulhee Lee
中科院分区：
文献类型：
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作者：
E. Choi;Chulhee Lee
提出了一种基于Bhattacharyya距离的特征提取方法。最近，据报道，一个准确的估计分类误差是可能的使用巴塔查里亚距离。在所提出的方法中，作者试图找到最小化高斯ML分类器的估计分类误差的特征向量。为了找到这样的特征向量，他们从任意的初始特征向量开始，并使用两种优化技术来更新它们：顺序搜索和全局搜索。由于它们使用误差估计方程来更新特征向量，因此可以显着减少搜索时间。他们首先将算法应用于两类问题，并将其扩展到多类问题。实验结果表明，该算法与传统的特征提取算法相比，具有较好的性能。
The authors propose a feature extraction method based on the Bhattacharyya distance. Recently, it has been reported that an accurate estimation of classification error is possible using the Bhattacharyya distance. In the proposed method, the authors try to find feature vectors that minimize the estimated classification error of Gaussian ML classifier. In order to find such feature vectors, they start with arbitrary initial feature vectors and update them using two optimization techniques: sequential search and global search. Since they use the error estimation equation for updating feature vectors, the search time can be reduced significantly. They first apply the algorithm to two class problems and extend it to multiclass problems. Experimental results show that the proposed feature extraction algorithm compares favorably with conventional feature extraction algorithms.