Kernel Two-Dimensional Subclass Discriminant Analysis

WANG Ying; LI Wen-hui; FU Bo; LIN Yi-feng; NI Hong-yin

doi:10.3969/j.issn.0372-2112.2013.05.025

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Kernel Two-Dimensional Subclass Discriminant Analysis

更新时间：2025-07-16

- Kernel Two-Dimensional Subclass Discriminant Analysis
- Acta Electronica Sinica Vol. 41, Issue 5, Pages: 992-996(2013)
- 作者机构：
  
  1. 吉林大学汽车仿真与控制国家重点实验室,吉林,长春,130012
  2. 吉林大学计算机科学与技术学院,吉林,长春,130012
  3. 常州大学信息科学与工程学院,江苏,常州,213000
  4. 吉林大学汽车仿真与控制国家重点实验室吉林长春,130012
  5. 吉林大学计算机科学与技术学院吉林长春,130012
  6. 常州大学信息科学与工程学院江苏常州,213000
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2013.05.025
  CLC： TP391
- Published：2013
- 稿件说明：
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WANG Ying, LI Wen-hui, FU Bo, et al. Kernel Two-Dimensional Subclass Discriminant Analysis[J]. Acta Electronica Sinica, 2013, 41(5): 992-996.
DOI：

WANG Ying, LI Wen-hui, FU Bo, et al. Kernel Two-Dimensional Subclass Discriminant Analysis[J]. Acta Electronica Sinica, 2013, 41(5): 992-996. DOI： 10.3969/j.issn.0372-2112.2013.05.025.

摘要

针对图像数据的多子类线性不可分问题

提出一种快速核二维子类鉴别分析算法(K2DSDA).算法从理论证明K2DSDA等价于基于核样本列(行)向量的2DSDA

并结合快速核样本图像近似定义实现最优判别向量计算

降低了算法复杂度.实验显示

本文算法在多个标准人脸库上的分类准确率优于其他鉴别分析算法

这证实了K2DSDA的有效性.

Abstract

Aiming to resolve the classification problem that image samples are multi-subclass distributed and non-linearly separable

a kernel two-dimensional subclass discriminant analysis algorithm (K2DSDA) is proposed.In this paper

it has shown that K2DSDA algorithm is theoretically equivalent to column/row-2DSDA based algorithm on kernel samples.Meanwhile

the optimal discriminant vectors are computed via the approximate kernel samples

so that the computational complexity is greatly reduced.The experimental results which tested on benchmark face database show that the proposed algorithm is superior to other state-of-the-art discriminant analysis algorithms

which confirms the effectiveness of K2DSDA.

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