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Author Fukui, K.-I. ♦ Ono, S. ♦ Megano, T. ♦ Numao, M.
Sponsorship IEEE Comput. Soc.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2013
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Special computer methods
Subject Keyword Measurement ♦ Smoothing methods ♦ Indexes ♦ Vectors ♦ Vehicles ♦ Entropy ♦ Iris ♦ differential evolution ♦ Mahalanobis distance ♦ clustering index ♦ self-organizing maps
Abstract This study proposes a distance metric learning method based on a clustering index with neighbor relation that simultaneously evaluates inter-and intra-clusters. Our proposed method optimizes a distance transform matrix based on the Mahalanobis distance by utilizing a self-adaptive differential evolution (jDE) algorithm. Our approach directly improves various clustering indices and in principle requires less auxiliary information compared to conventional metric learning methods. We experimentally validated the search efficiency of jDE and the generalization performance.
Description Author affiliation: Grad. Sch. of Sci. & Eng., Kagoshima Univ., Kagoshima, Japan (Ono, S.; Megano, T.) || Inst. of Sci. & Ind. Res., Osaka Univ., Ibaraki, Japan (Fukui, K.-I.; Numao, M.)
ISSN 10823409
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2013-11-04
Publisher Place USA
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
e-ISBN 9781479929726
Size (in Bytes) 747.57 kB
Page Count 6
Starting Page 398
Ending Page 403

Source: IEEE Xplore Digital Library