基于模糊核匹配追寻的特征模式识别

Pattern Recognition Based on the Fuzzy Kernel Matching Pursuit

核匹配追寻算法是近年来新兴的模式识别方法,在处理非线性及高维模式识别问题中表现出了突出的优点.传统的核匹配追寻在处理模式识别的问题中平等地对待所有样本,最终的判决函数是针对所有样本的一个平等综合考虑,要求总识别误差尽可能小,并不能对某一类指定样本进行针对性识别,然而实际应用中经常会碰到这样的情况:要求对某一类样本的识别精度很高,尤其是对于非平衡样本中或者对于具有时间属性的样本序列,由于标准核匹配追寻学习机自身的局限性,使其不能有效地处理这些问题.文中针对这些问题,提出了模糊核匹配追寻学习机,预先根据分类的要求对每个样本做出了不同的重要性定义,学习机根据重要性不同,对样本进行程度不同的学习,最终得到基于问题的判决——对重要样本保持很高的分类精度;最后通过实际的仿真实验证明了模糊匹配追寻的有效性及可行性.

Kernel Matching Pursuit （KMP）, a novel method of the pattern recognition, presents excellent performance in solving the problems with small sample, nonlinear and local minima. KMP has been proposed to provide a good generalization performance for both classes, yet the classification precision of some important data can＇t be classified precisely. Because the decision function found by KMP is the synthetic consideration results of all the data, it has greatly limited its use in many practical problems, such as time series identification and unbalanced data classification. In this paper, an fuzzy kernel matching pursuit machine is （FKMP） proposed, which can classify the appointed important samples much more precisely according to the predefined importance of the data. Lots of experiments have been given in the paper to prove the feasibility and validation of the fuzzy kernel matching pursuit machine.