计算机工程 ›› 2009, Vol. 35 ›› Issue (23): 207-208,.doi: 10.3969/j.issn.1000-3428.2009.23.071

• 人工智能及识别技术 • 上一篇    下一篇

基于有监督流形学习的正交投影降维

蒋 润,周激流,雷 刚,李晓华   

  1. (四川大学计算机学院,成都 610064)
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2009-12-05 发布日期:2009-12-05

α-based Supervised Orthogonal Projection Reduction by Affinity

JIANG Run, ZHOU Ji-liu, LEI Gang, LI Xiao-hua   

  1. (School of Computer, Sichuan University, Chengdu 610064)
  • Received:1900-01-01 Revised:1900-01-01 Online:2009-12-05 Published:2009-12-05

摘要: 将监督局部线性嵌入的思想引入传统的正交投影降维方法(OPRA)方法,提出一种新的基于有监督流形学习的正交投影降维方法(α-OPRA),使高维到低维的映射在保留某些流形结构的同时,进一步获得较好的正交投影效果。该方法通过加入额外的参数α来控制监督的程度,在纯粹的有监督的OPRA和无监督的OPRA之间取得了某些折中。实验结果证明,该方法能获得较好的降维结果。

关键词: 正交投影降维方法, 降维, 人脸识别

Abstract: This paper introduces the idea of SLLE into the traditional method of OPRA, which proposes a new approach of α-based Supervised Orthogonal Projection Reduction by Affinity(α-OPRA) for dimension reduction. Such method keeps the reservations of some flow-shaped structure during high-dimensional to low-dimensional mapping, gets better orthogonal projection. The method by adding additional parameters to control the degree of supervision, so in a purely supervised OPRA and unsupervised OPRA between there has been some compromise. Experimental results show that this method can get better reduction result.

Key words: Orthogonal Projection Reduction by Affinity(OPRA), dimension reduction, face recognition

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