The Minimum Entropy Alignment in Manifold Learning

Chen Jing; Liu Yang

doi:10.3969/j.issn.1007-7162.2015.03.008

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The Minimum Entropy Alignment in Manifold Learning

更新时间：2026-05-27

- The Minimum Entropy Alignment in Manifold Learning
- Vol. 32, Issue 3, Pages: 39-45(2015)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1007-7162.2015.03.008
  CLC：
- Online First：22 September 2015，
  
  Published：2015
- 稿件说明：
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陈静, 刘洋. 基于最小熵的流形学习排列方法[J]. 广东工业大学学报, 2015, 32(3): 39-45.

Chen Jing, Liu Yang. The Minimum Entropy Alignment in Manifold Learning[J]. 2015, 32(3): 39-45.
陈静, 刘洋. 基于最小熵的流形学习排列方法[J]. 广东工业大学学报, 2015, 32(3): 39-45. DOI： 10.3969/j.issn.1007-7162.2015.03.008.

Chen Jing, Liu Yang. The Minimum Entropy Alignment in Manifold Learning[J]. 2015, 32(3): 39-45. DOI： 10.3969/j.issn.1007-7162.2015.03.008.

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.

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.

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