Journal of Computer Applications ›› 2019, Vol. 39 ›› Issue (5): 1453-1458.DOI: 10.11772/j.issn.1001-9081.2018102113

• Virtual reality and multimedia computing • Previous Articles     Next Articles

Robust multi-manifold discriminant local graph embedding based on maximum margin criterion

YANG Yang1, WANG Zhengqun1, XU Chunlin2, YAN Chen1, JU Ling1   

  1. 1. School of Information Engineering, Yangzhou University, Yangzhou Jiangsu 225127, China;
    2. North Laser Technology Group Corporation Limited, Yangzhou Jiangsu 225009, China
  • Received:2018-10-19 Revised:2018-12-02 Online:2019-05-10 Published:2019-05-14
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61402395), the Prospective Joint Research Project of Jiangsu Province (BY201506-01).


杨洋1, 王正群1, 徐春林2, 严陈1, 鞠玲1   

  1. 1. 扬州大学 信息工程学院, 江苏 扬州 225127;
    2. 北方激光科技集团有限公司, 江苏 扬州 225009
  • 通讯作者: 王正群
  • 作者简介:杨洋(1990-),男,江苏扬州人,硕士研究生,主要研究方向:模式识别;王正群(1965-),男,江苏如东人,教授,博士,主要研究方向:模式识别、机器学习;徐春林(1969-),男,江苏兴化人,研究员级高级工程师,硕士,主要研究方向:信号处理;严陈(1993-),男,江苏如东人,硕士研究生,主要研究方向:模式识别;鞠玲(1994-),女,江苏扬州人,硕士研究生,主要研究方向:模式识别。
  • 基金资助:

Abstract: In most existing multi-manifold face recognition algorithms, the original data with noise are directly processed, but the noisy data often have a negative impact on the accuracy of the algorithm. In order to solve the problem, a Robust Multi-Manifold Discriminant Local Graph Embedding algorithm based on the Maximum Margin Criterion (RMMDLGE/MMC) was proposed. Firstly, a denoising projection was introduced to process the original data for iterative noise reduction, and the purer data were extracted. Secondly, the data image was divided into blocks and a multi-manifold model was established. Thirdly, combined with the idea of maximum margin criterion, an optimal projection matrix was sought to maximize the sample distances on different manifolds while to minimize the sample distances on the same manifold. Finally, the distance from the test sample manifold to the training sample manifold was calculated for classification and identification. The experimental results show that, compared with Multi-Manifold Local Graph Embedding algorithm based on the Maximum Margin Criterion (MLGE/MMC) which performs well, the classification recognition rate of the proposed algorithm is improved by 1.04, 1.28 and 2.13 percentage points respectively on ORL, Yale and FERET database with noise and the classification effect is obviously improved.

Key words: multi-manifold, denoising projection, graph embedding, maximum margin criterion, classification and identification

摘要: 针对现有的多流形人脸识别算法大多直接使用带有噪声的原始数据进行处理,而带有噪声的数据往往会对算法的准确率产生负面影响的问题,提出了一种基于最大间距准则的鲁棒多流形判别局部图嵌入算法(RMMDLGE/MMC)。首先,通过引入一个降噪投影对原始数据进行迭代降噪处理,提取出更加纯净的数据;其次,对数据图像进行分块,建立多流形模型;再次,结合最大间隔准则的思想,寻求最优的投影矩阵使得不同流形上的样本距离尽可能大,同时相同流形上的样本距离尽可能小;最后,计算待识样本流形到训练样本流形的距离进行分类识别。实验结果表明,与表现较好的最大间距准则框架下的多流形局部图嵌入算法(MLGE/MMC)相比,所提算法在添加噪声的ORL、Yale和FERET库上的分类识别率分别提高了1.04、1.28和2.13个百分点,分类效果明显提高。

关键词: 多流形, 降噪投影, 图嵌入, 最大间隔准则, 分类识别

CLC Number: