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

Zhu, R. ORCID: 0000-0002-9944-0369, Dong, M. & Xue, J-H. (2018). Learning distance to subspace for the nearest subspace methods in high-dimensional data classification. Information Sciences, 481, pp. 69-80. doi: 10.1016/j.ins.2018.12.061


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.

Publication Type: Article
Additional Information: © 2018 Elsevier Inc. This manuscript version is made available under the CC-BY-NC-ND 4.0 license
Publisher Keywords: Nearest subspace methods (NSM); Distance to subspace; Distance metric learning Orthogonal distance; Score distance
Subjects: H Social Sciences > HA Statistics
Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science
Departments: Bayes Business School > Actuarial Science & Insurance
Text - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

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