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Building a discriminatively ordered subspace on the generating matrix to classify high-dimensional spectral data

Zhu, R. ORCID: 0000-0002-9944-0369, Fukui, K. & Xue, J-H. (2017). Building a discriminatively ordered subspace on the generating matrix to classify high-dimensional spectral data. Information Sciences, 382-38, pp. 1-14. doi: 10.1016/j.ins.2016.12.001


Soft independent modelling of class analogy (SIMCA) is a widely-used subspace method for spectral data classification. However, since the class subspaces are built independently in SIMCA, the discriminative between-class information is neglected. An appealing remedy is to first project the original data to a more discriminative subspace. For this, generalised difference subspace (GDS) that explores the information between class subspaces in the generating matrix can be a strong candidate. However, due to the difference between a class subspace (of infinite scale) and a class (of finite scale), the eigenvectors selected by GDS may not also be discriminative for classifying samples of classes. Therefore in this paper, we propose a discriminatively ordered subspace (DOS): different from GDS, our DOS selects the eigenvectors with high discriminative ability between classes rather than between class subspaces. The experiments on three real spectral datasets demonstrate that applying DOS before SIMCA outperforms its counterparts.

Publication Type: Article
Additional Information: © 2016 Elsevier inc. This manuscript version is made available under the CC-BY-NC-ND 4.0 license
Publisher Keywords: Discriminatively ordered subspace, Generalised difference subspace, Generating matrix, SIMCA, Spectral data classification, Subspace method
Subjects: H Social Sciences > HA Statistics
Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science
Departments: Bayes Business School > Actuarial Science & Insurance
SWORD Depositor:
[thumbnail of RuiZhu-INS-2016-UCL.pdf] Text - Accepted Version
This document is not freely accessible due to copyright restrictions.
Available under License Creative Commons Attribution Non-commercial No Derivatives.


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