数据采集与处理2018，Vol.33Issue(3)：446-454,9.DOI:10.16337/j.1004-9037.2018.03.007
极速非线性判别分析网络
Extreme Nonlinear Discriminant Analysis Network
摘要
Abstract
As the linear discriminant analysis (LDA ) is just a linear method and is difficult to effectively deal with nonlinear problems ,non-linearizing LDA is a crucial strategy to enable it to solve such nonlinear problems .Nonlinear LDA is mainly based on two strategies ,neural networks and kernelization .A repre-sentative of the former strategy is the neural network discriminant analysis (NNDA ) .Athough NNDA inherits the advantages such as self-adaption ,parallel processing ,distributed storing and nonlinear map-ping of neural networks ,its training is quite time-consuming and likely to get trapped in local minimum . While the representative of the latter strategy is the kernel linear discriminant analysis (KLDA ) .Al-though KLDA can obtain a global optimal analytical solution ,its computational cost is rather high ,due to the fact that the number of hidden nodes of KLDA is equal to the size of training samples ,especially in large scale scenarios .Inspired by the idea of random map ,a novel extreme nonlinear discriminant analy-sis (ENDA ) is proposed by reconstructing NNDA via extreme learning strategy in this paper .ENDA shares both the self-adaption of NNDA and the efficient computation of global optimal solution of KLDA . Finally ,experimental results on UCI datasets demonstrate the superiority of ENDA over KLDA and NN-DA in classification accuracy .关键词
线性判别分析/神经网络/核判别分析/极速化Key words
linear discriminant analysis/neural network/kernel discriminant analysis/speedup分类
信息技术与安全科学引用本文复制引用
谢群辉,陈松灿..极速非线性判别分析网络[J].数据采集与处理,2018,33(3):446-454,9.基金项目
国家自然科学基金(61472186)资助项目 （61472186）
中国博士后科学基金(20133218110032)资助项目. （20133218110032）
