基于模糊隶属度的加权广义不定核判别分析

对线性不可分的问题,已有许多基于正定核的降维方法,Fisher判别分析法是其中常用的方法。本研究对此类方法进行了改进和推广,首先将正定核推广到不定核,然后提出了基于模糊隶属度的不定核判别分析,最后结合权函数提出了加权广义不定核判别分析。实验结果表明,所提算法不仅有很好的分类效果,而且权函数的选择对分类结果有比较明显的影响。

Weighted generalized indefinite kernel discriminant analysis based on fuzzy memberships

For linear non-separated problems,many dimensionality reduction methods which are based on the definite kernel were proposed.The Fisher discriminant analysis method,one of the commonly used methods,was improved and extended.The definite kernel was extended to the indefinite kernel,and then indefinite kernel discriminant analysis based on fuzzy memberships was proposed.In addition,weighted generalized IKDA algorithms were achieved according to the weighting function.Experimental results showed that the proposed methods could achieve good classification results,and the choice of the weighting function could have a significant effect on the classification results.