NDLI: A Study of Distance Metric Learning by Considering the Distances between Category Centroids

Content Provider	IEEE Xplore Digital Library
Author	Mikawa, K. Kobayashi, M. Goto, M. Hirasawa, S.
Copyright Year	2015
Description	Author affiliation: Dept. of Creative & Eng., Waseda Univ., Tokyo, Japan (Mikawa, K.; Goto, M.) \|\| Dept. of Inf. Manage. Sci., Shonan Inst. of Technol., Shonan, Japan (Kobayashi, M.) \|\| Res. Inst. for Sci. & Eng., Waseda Univ., Tokyo, Japan (Hirasawa, S.)
Abstract	In this paper, we focus on pattern recognition based on the vector space model. As one of the methods, distance metric learning is known for the learning metric matrix under the arbitrary constraint. Generally, it uses iterative optimization procedure in order to gain suitable distance structure by considering the statistical characteristics of training data. Most of the distance metric learning methods estimate suitable metric matrix from all pairs of training data. However, the computational cost is considerable if the number of training data increases in this setting. To avoid this problem, we propose the way of learning distance metric by using the each category centroid. To verify the effectiveness of proposed method, we conduct the simulation experiment by using benchmark data.
Starting Page	1645
Ending Page	1650
File Size	245831
Page Count	6
File Format	PDF
e-ISBN	9781479986972
DOI	10.1109/SMC.2015.290
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-10-09
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Measurement Training data Optimization Matrix decomposition Pattern recognition Learning systems Correlation Regularization Distance Metric Learning Vector Space Model Pattern Recognition
Content Type	Text
Resource Type	Article

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