改进的核子类判决分析

Improved kernel clustering-based discriminant analysis

提出了改进的核子类判决分析(improvcd kernel clustering-based discriminant analysis,IKCDA)方法,首先采用快速全局核k-均值聚类算法找到每类目标的最优子类划分,然后基于找到的子类划分结果采用核子类判决分析求取最优的投影矢量。基于UCI机器学习数据库的实验结果表明,经过IKCDA特征提取后异类样本间的可分性明显改善了。此外,基于美国运动和静止目标获取与识别(moving and stationary target acquisitionand recognition,MSTAR)计划录取的合成孔径雷达地面静止目标数据的实验结果表明,经过IKCDA后可以改善对真实目标的分类性能和对干扰目标的拒判能力。

An improved kernel clustering-based discriminant analysis（IKCDA）method is proposed.The data for each class is firstly partitioned into multiple clusters via the fast global kernel k-means clustering algorithm, and then the optimal projection vectors are found based on these clusters.Experimental results performing on the UCI machine learning dataset demonstrate that samples belonging to different classes are more separable by the IKCDA method.Moreover,experimental results performing on synthetic aperture radar ground stationary targets based the moving and stationary target acquisition and recognition（MSTAR）public database also indicate that the classification capabilities of the true objects classes and the rejection capabilities of the confusers classes can be bettered via the IKCDA method.