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计算机工程 ›› 2011, Vol. 37 ›› Issue (17): 178-180,184. doi: 10.3969/j.issn.1000-3428.2011.17.060

• 人工智能及识别技术 • 上一篇    下一篇

引入指数p的局部保持映射算法

安亚静a,王士同b   

  1. (江南大学 a. 物联网工程学院;b. 数字媒体学院,江苏 无锡 214122)
  • 收稿日期:2011-02-15 出版日期:2011-09-05 发布日期:2011-09-05
  • 作者简介:安亚静(1986-),女,硕士研究生,主研方向:局部保持映射算法,人工智能,模式识别;王士同,教授、博士生导师
  • 基金资助:
    国家自然科学基金资助项目(60773206)

Local Preserving Projection Algorithm with Exponential p

AN Ya-jing  a, WANG Shi-tong  b   

  1. (a. School of Internet of Things Engineering; b. School of Digital Media, Jiangnan University, Wuxi 214122, China)
  • Received:2011-02-15 Online:2011-09-05 Published:2011-09-05

摘要: 在进行降维时数据集合的多样性要求降维算法求解问题具有灵活性。为此,利用加入指数 来调节局部保持映射算法的约束条件,通过实验观察该指数的引入对降维以及识别率的影响,并总结指数 的范围和设计经验。实验结果表明,指数 可以影响降维效果,使维数降得更低,通过调节提高人脸识别率,在加入高斯白噪声后通过调节指数p也可改善识别的效果。

关键词: 局部保持映射, 流形学习, 邻接图, 约束条件, 噪声

Abstract: As the diversity of the data collection, it is necessary to enhance the flexibility of the algorithm when reduce the dimensions of the data sets. The article changes the constraints of the Local Preserving Projection(LPP) algorithm by adding an exponential parameter p. Then can see the result of dimension reduction and look over the recognition rate through different face databases. The article also attempts to summarize the scope of p and design experience. Experimental show that the exponential parameter does influent the dimension reduction results. The dimension can be lower and if select the proper p the result will be better. If add gussian white noise, the consequent is still better by adjusting the exponential parameter p.

Key words: Local Preserving Projection(LPP), manifold learning, adjaceny graph, constraints, noise

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