[1]GUO Yingjie,WANG Shitong,XU Xiaolong.Learning a linear combination of distances based on the maximum-margin theory[J].CAAI Transactions on Intelligent Systems,2015,10(6):843-850.[doi:10.11992/tis.201504027]
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Learning a linear combination of distances based on the maximum-margin theory

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 10 Number of periods: 2015 6 Page number: 843-850 Column: 学术论文—人工智能基础 Public date: 2015-12-25
Title:
Learning a linear combination of distances based on the maximum-margin theory
Author(s):
GUO Yingjie;  WANG Shitong;  XU Xiaolong
School of Digital Media, Jiangnan University, Wuxi 214000, China
Keywords:
metric learning; hybrid distance metric; maximum-margin theory; fuzzy C-means; fuzzy clustering; clustering algorithm; metric; learning algorithm
CLC:
TP181
DOI:
10.11992/tis.201504027
Abstract:
Learning a distance metric from given training samples is a crucial aspect of many machine learning tasks. Conventional distance metric learning approaches often assume the target distance function to be represented in the form of Mahalanobis distance, and the metric has limitations for this application. This paper proposes a new metric learning approach in which the target distance function is represented as a linear combination of several candidate distance metrics. This method obtains a new distance metric by learning weights from side information according to the maximum-margin theory. The new distance function is applied to fuzzy C-means clustering for evaluation. The experiments were performed using UCI data, and a comparison of the results with those of other approaches reveals the advantages of the proposed technique.
References:
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