【摘要】 线性判别回归分类算法没有考虑到类内距离和类间距离,为此提出一种基于线性判别回归与局部判别分析的维数约简算法(LDRFDR),同时利用类内和类间重构误差、以及类内和局部类间距离获得投影矩阵。其物理含义是为各个类尽量寻找相互之间离得最远的线性子空间,其中类内距离与类间距离还考虑数据的局部性,避免最大化相离太远的类间样本对优化目标造成影响。实验结果表明,LDRFDR算法的维数约简性能优于其它半监督维数约减算法。更多还原

【Abstract】 To overcome the problem that linear discriminant regression classification(LDRC)does not consider the within class distances and across classes distances,a dimensionality reduction method named linear discriminant regression and local discriminant analysis based dimensionality reduction(LDRFDR)was proposed.The within-class reconstruction error,across classes reconstruction error,within class distances,and across classes distances were utilized at the same time.The physical meaning of LDRFDR was to find the farthest linear space among all classes.In LDRFDR,the locality was also considered by within class distances and across classes distances,which avoided the impact of the distance between two far-away-samples.Experimental results demonstrate that LDRFDR is better than other semi-supervised dimensionality reduction algorithms.更多还原