Metric Learning in Dissimilarity Space for Improved Nearest Neighbor Performance

被引：0

作者：

Duin, Robert P. W. ^{[1
]}

Bicego, Manuele ^{[2
]}

Orozco-Alzate, Mauricio ^{[3
]}

Kim, Sang-Woon ^{[4
]}

Loog, Marco ^{[1
]}

机构：

[1] Delft Univ Technol, PRLab, NL-2600 AA Delft, Netherlands

[2] Univ Verona, Dept Comp Sci, I-37134 Verona, Italy

[3] Univ Nacl Colombia, Dept Informat & Computac, Sede Manizales, Colombia

[4] Myongji Univ, Dept Comp Sci & Engn, Yongin 449728, South Korea

来源：

STRUCTURAL, SYNTACTIC, AND STATISTICAL PATTERN RECOGNITION | 2014年 / 8621卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Showing the nearest neighbor is a useful explanation for the result of an automatic classification. Given, expert defined, distance measures may be improved on the basis of a training set. We study several proposals to optimize such measures for nearest neighbor classification, explicitly including non-Euclidean measures. Some of them may directly improve the distance measure, others may construct a dissimilarity space for which the Euclidean distances show significantly better performances. Results are application dependent and raise the question what characteristics of the original distance measures influence the possibilities of metric learning.

引用

页码：183 / 192

页数：10

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