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Learning Local Metrics and Influential Regions for Classification

Dong, M; Wang, Y; Yang, X; Xue, J-H; (2019) Learning Local Metrics and Influential Regions for Classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 10.1109/tpami.2019.2914899. (In press). Green open access

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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.

Type: Article
Title: Learning Local Metrics and Influential Regions for Classification
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/tpami.2019.2914899
Publisher version: https://doi.org/10.1109/tpami.2019.2914899
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Measurement , Task analysis , Learning systems , Mathematical model , Fasteners , Artificial neural networks , Clustering algorithms , Distance-based classification , distance metric , metric learning , local metric
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/10078217
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