广东工业大学学报 ›› 2015, Vol. 32 ›› Issue (3): 39-45.doi: 10.3969/j.issn.1007-7162.2015.03.008

• 综合研究 • 上一篇    下一篇

基于最小熵的流形学习排列方法

陈静1，刘洋2   

  1. 广东工业大学 1.物理与光电工程学院；2.信息工程学院，广东 广州 510006
  • 收稿日期:2014-05-13 出版日期:2015-09-22 发布日期:2015-09-22
  • 作者简介:陈静（1980-），女，副教授，博士，主要研究方向为机器学习.
  • 基金资助:

    国家自然科学基金资助项目（61305069）；广东省自然科学基金资助项目（S2013040016371）

The Minimum Entropy Alignment in Manifold Learning

Chen Jing1, Liu Yang2   

  1. 1. School of Physics and Optoelectronic Engineering; 2. School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2014-05-13 Online:2015-09-22 Published:2015-09-22

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摘要： 提出一种渐进式的排列方法：每次只是排列当前阶段交集最大的二个分片的局部坐标.该方法具有以下特点：方法简单，避免了全局排列方法中大型稀疏矩阵的特征值问题；每次排列都保证是误差最小的排列；减轻误差的积累和传播.

关键词: 流形学习； 降维； 排列

Abstract: A new progressive alignment method is proposed, while only the local coordinates of the two patches with the largest intersection at the current stage of progressive alignment will be aligned into a larger local coordinate. The method has the following features: without needing to solve a largescale eigenvalues problem and suffering from the problem of local minima; less accumulation and propagation of alignment errors.

Key words: manifold learning; dimensionality reduction; alignment

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