Learning local metrics and influential regions for classification

Dong, M., Wang, Y., Yang, X. and Xue, J.-H. (2020) Learning local metrics and influential regions for classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(6), pp. 1522-1529. (doi: 10.1109/TPAMI.2019.2914899)

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Abstract

The performance of distance-based classifiers heavily depends on the underlying distance metric, so it is valuable to learn a suitable metric from the data. To address the problem of multimodality, it is desirable to learn local metrics. In this short paper, we define a new intuitive distance with local metrics and influential regions, and subsequently propose a novel local metric learning algorithm called LMLIR for distance-based classification. Our key intuition is to partition the metric space into influential regions and a background region, and then regulate the effectiveness of each local metric to be within the related influential regions. We learn multiple local metrics and influential regions to reduce the empirical hinge loss, and regularize the parameters on the basis of a resultant learning bound. Encouraging experimental results are obtained from various public and popular data sets.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Yang, Ms Xiaochen
Authors: Dong, M., Wang, Y., Yang, X., and Xue, J.-H.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:IEEE Transactions on Pattern Analysis and Machine Intelligence
Publisher:IEEE
ISSN:0162-8828
ISSN (Online):1939-3539
Published Online:03 May 2019
Copyright Holders:Copyright © 2019 IEEE
First Published:First published in IEEE Transactions on Pattern Analysis and Machine Intelligence 42(6): 1522-1529
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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