计算机工程 ›› 2011, Vol. 37 ›› Issue (24): 193-194.doi: 10.3969/j.issn.1000-3428.2011.24.064

• 人工智能及识别技术 • 上一篇    下一篇

基于成对约束和稀疏保留的数据降维算法

王颖静,王正群,张国庆,俞振洲   

  1. (扬州大学信息工程学院,江苏 扬州 225009)
  • 收稿日期:2011-07-19 出版日期:2011-12-20 发布日期:2011-12-20
  • 作者简介:王颖静(1987-),女,硕士研究生,主研方向:机器学习;王正群,教授、博士;张国庆、俞振洲,硕士研究生
  • 基金项目:

    国家自然科学基金资助项目(60875004);江苏省自然科学基金资助项目(BK2009184);江苏省高校自然科学基金资助项目(10KJB510027, 07KJB520133)

Dimensionality Reduction Algorithm Based on Pair-wise Constraints and Sparsity Preserving

WANG Ying-jing, WANG Zheng-qun, ZHANG Guo-qing, YU Zhen-zhou   

  1. (Collage of Information Engineering, Yangzhou University, Yangzhou 225009, China)
  • Received:2011-07-19 Online:2011-12-20 Published:2011-12-20

摘要: 结合以成对约束形式给出的监督信息和无监督信息,提出一种基于成对约束和稀疏保留的数据降维算法。通过成对约束信息进行鉴别分析,利用稀疏表示方法保留数据集在变换空间中的全局稀疏结构。实验结果表明,与传统特征抽取算法相比,该算法的识别效果更好,需要调节的参数更少,且鲁棒性较高。

关键词: 稀疏保留, 机器学习, 特征提取, 人脸识别

Abstract: This paper presents a dimensionality reduction algorithm based on pair-wise constraints and sparsity preserving. It combines some supervised information in the form of pair-wise constraints and large number of unsupervised information. It uses pair-wise constraints to discriminant analysis and uses sparse representation to preserve the sparse reconstructive structure in the transformed space. Compared with the traditional feature extraction method, this algorithm has a better recognition impact, lower parameters, and better robustness.

Key words: sparsity preserving, machine learning, feature extraction, face recognition

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