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Learning distance to subspace for the nearest subspace methods in high-dimensional data classification

Zhu, R; Dong, M; Xue, JH; (2019) Learning distance to subspace for the nearest subspace methods in high-dimensional data classification. Information Sciences , 481 pp. 69-80. 10.1016/j.ins.2018.12.061.

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Abstract

The nearest subspace methods (NSM) are a category of classification methods widely applied to classify high-dimensional data. In this paper, we propose to improve the classification performance of NSM through learning tailored distance metrics from samples to class subspaces. The learned distance metric is termed as ‘learned distance to subspace’ (LD2S). Using LD2S in the classification rule of NSM can make the samples closer to their correct class subspaces while farther away from their wrong class subspaces. In this way, the classification task becomes easier and the classification performance of NSM can be improved. The superior classification performance of using LD2S for NSM is demonstrated on three real-world high-dimensional spectral datasets.

Type: Article
Title: Learning distance to subspace for the nearest subspace methods in high-dimensional data classification
DOI: 10.1016/j.ins.2018.12.061
Publisher version: https://doi.org/10.1016/j.ins.2018.12.061
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: Nearest subspace methods (NSM), Distance to subspace, Distance metric learning, Orthogonal distance, Score distance
UCL classification: 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: http://discovery.ucl.ac.uk/id/eprint/10066624
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