华东师范大学学报(自然科学版) ›› 2017, Vol. 2017 ›› Issue (2): 69-74,88.doi: 10.3969/j.issn.1000-5641.2017.02.009

• 计算机科学 • 上一篇    下一篇

基于维度约束的距离测度学习算法

房娟, 刘洪英, 李庆利   

  1. 华东师范大学 信息与科学技术学院 上海市多维度信息处理重点实验室, 上海 200241
  • 收稿日期:2015-07-08 出版日期:2017-03-25 发布日期:2017-03-23
  • 通讯作者: 刘洪英,女,副教授,研究方向为多维信息处理.E-mail:hyliu@ee.ecnu.edu.cn E-mail:hyliu@ee.ecnu.edu.cn
  • 作者简介:房娟,女,硕士研究生,研究方向为多维信息处理.E-mail:fjoanna@126.com
  • 基金资助:

    国家自然科学基金（61240006）

Learning distance metrics with dimension constraints

FANG Juan, LIU Hong-ying, LI Qing-li   

  1. School of Information Technology, Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai 200241, China
  • Received:2015-07-08 Online:2017-03-25 Published:2017-03-23

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摘要：

为提高分类精度，通过距离测度学习可以得到样本在新的特征空间里新的表示.针对马氏距离未考虑不同类别样本维度间相关性存在差异这一缺陷，提出了一种新的有监督的距离测度学习算法，即独立-差别分量分析方法（Independent Discrimi-Native Component Analysis，I-DCA），并将其运用于基于k近邻分类器的运动神经与感觉神经分类中.作为对照，还详细分析了已有的相关分量分析方法（Relevant Component Analysis，RCA）和差别分量分析方法（Discrimi-Native Component Analysis，DCA）这两种距离测度学习算法.实验结果表明，改进算法的分类精度相较于马氏距离提高了近45%相较于RCA与DCA也提高了15%左右，分类精度的提高说明了改进算法的有效性.

关键词: 距离测度学习, 有监督学习, k近邻分类, 显微高光谱, 神经分类

Abstract:

In order to improve the classification accuracy, the new representation of samples can be gotten by distance metric learning. According to mahalanobis distance does not take the difference of the relativity between different classes of sample dimensions into consideration. A new supervised distance metric learning algorithm called independent discrimi-native component analysis(I-DCA) is proposed and applied to classify the motor and sensory nerve based on k nearest neighbor (kNN) algorithm. By contrast, the article also involves the analysis of two existing distance metric learning algorithms in detail, the relevant component analysis (RCA) and the discrimi-native component analysis(DCA). Compared with the mahalanobis distance, the results indicate that the classification precision of the improved algorithm increases by nearly 45%, and it is also greater than 15% compared to the RCA and DCA method. The improved classification precision shows the effectiveness of the new algorithm applied in nerve classification.

Key words: distance metric learning, supervised learning, kNN classification, microscopic hyperspectral imaging, neural classification

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