基于维度约束的距离测度学习算法

房娟; 刘洪英; 李庆利

doi:10.3969/j.issn.1000-5641.2017.02.009

基于维度约束的距离测度学习算法

doi: 10.3969/j.issn.1000-5641.2017.02.009

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

基金项目:

国家自然科学基金 61240006

详细信息

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

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

中图分类号: TP181
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- 文章访问数: 279
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- 被引次数: 0
出版历程
- 收稿日期: 2015-07-08
- 刊出日期: 2017-03-25

Learning distance metrics with dimension constraints

School of Information Technology, Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai 200241, China

摘要

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.
- 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.
- distance metric learning /
- supervised learning /
- kNN classification /
- microscopic hyperspectral imaging /
- neural classification

HTML全文

图 1 空间变换示意图

Fig. 1 Schematic illustration of the space transformation

下载: 全尺寸图片幻灯片

图 2 RCA与DCA算法说明

(a) 带有标签信息的原始数据集合; (b) 经RCA变换后的数据集合; (c) 经DCA变换后的数据集合

Fig. 2 An illustrative example of the RCA and DCA algorithm

(a) The fully labeled data set with 3 classes; (b) The original data after applying the RCA transformation; (c) The original data after applying the DCA transformation.

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图 3 I-DCA算法说明

(a) 原始数据集合; (b) 随机选择的带有标签信息的训练样本; (c) 变换后各维度相互独立的训练样本 (d) 带有标签信息的原始数据集; (e) 经马氏距离变换后的数据集; (f) 经I-DCA算法变换后的数据集

Fig. 3 An illustrative example of the I-DCA algorithm

(a) The fully unlabeled data set; (b) Random labeled train data set; (c) The whitening transformation applied to train data; (d) The fully labeled data set with 3 classes; (e) The original data after applying the mahalanobis distance; (f) The original data after applying the I-DCA transformation.

下载: 全尺寸图片幻灯片

表 1 数据类别名称以及数量

Tab. 1 The data type name and quantity

标号	类别名称
1	Motor_axone (2000)
2	Motor_medullary_sheath (2000)
3	Sensory_axone (2000)
4	Sensory _medullary_sheath (2000)

下载: 导出CSV

表 2 近邻总体分类精度比较

Tab. 2 Overall kNN classification precision comparing

	train% \| test%	Euclidean distance%	Mahalanobis distance%	I-DCA%	RCA%	DCA%
k=1	10\|90	86.5	53.61	86.28	69.92	71.83
	30\|70	94.16	59.41	93.87	74.42	77.33
	50\|50	96.3	62.42	95.98	75.81	78.31
k=5	10\|90	81.72	53.26	81.47	72.07	73.07
	30\|70	90.34	61.70	90.26	77.16	78.27
	50\|50	93.65	65.09	93.38	79.59	79.53
k=10	10\|90	77.66	51.67	77.16	70.81	73.12
	30\|70	86.79	58.77	86.63	75.81	77.22
	50\|50	90.63	63.81	90.57	77.62	79.32

下载: 导出CSV

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