基于次特征值误差补偿和非对称分布的马氏距离改进算法

Modified Mahalanobis Distance by Compensation for Errors of Non-dominant Eigenvalues and Asymmetrical Distribution

本文提出了一种有限样本集上基于次特征值误差补偿和优势主向量上非对称分布的马氏距离改进算法.通过改进的马氏距离,有限样本导致的次特征值误差得到补偿,样本特征矢量在变换空间的各优势主向量上的投影分布得到更精确的刻画,因此可以有效地计算最近邻参考矢量.在UCI手写体数字字符数据库上的识别实验结果表明,该改进算法对于提高识别性能是有效的.

A modification on Mahalanobis distance on samples of limited size by compensation for errors of non-dominant eigenvalues and asymmetrical distribution on dominant principle components is proposed. By the introduction of modified Mahalanobis distance,one can compute efficiently the nearest neighbor in transformed space, with compensation for errors of non-dominant eigenvalues and more accurate characterization of the projection distribution of feature vector on every dominant principal component. Experimental results on UCI dataset for handwritten digit recognition indicate that modified algorithm is effective to improve the recognition performance.