Feature Selection based on the Bhattacharyya Distance
Feature Selection based on the Bhattacharyya Distance
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DOI：
10.1109/icpr.2006.558
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发表时间：
2006-08
期刊：
影响因子：
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通讯作者：
Guorong Xuan;Xiuming Zhu;Peiqi Chai;Zhenping Zhang;Y. Shi;Dongdong Fu
中科院分区：
文献类型：
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作者：
Guorong Xuan;Xiuming Zhu;Peiqi Chai;Zhenping Zhang;Y. Shi;Dongdong Fu
提出了一种基于Bhattacharyya距离的多类分类问题特征选择方法，该方法利用递归算法获得了最优降维矩阵，该降维矩阵满足正态分布下分类误差的最小上界.在我们的方案中，PCA被纳入作为一个预处理，以减少棘手的沉重的计算负担的递归算法。在MNIST数据库和隐写分析应用中的手写数字识别实验结果表明，该方法具有良好的上级性能
This paper presents a Bhattacharyya distance based feature selection method, which utilizes a recursive algorithm to obtain the optimal dimension reduction matrix in terms of the minimum upper bound of classification error under normal distribution for multi-class classification problem. In our scheme, PCA is incorporated as a pre-processing to reduce the intractably heavy computation burden of the recursive algorithm. The superior experimental results on the handwritten-digit recognition with the MNIST database and the steganalysis applications have demonstrated the effectiveness of our proposed method