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On the orthogonal distance to class subspaces for high-dimensional data classification

Zhu, Rui, Xue, Jing-Hao (2017) On the orthogonal distance to class subspaces for high-dimensional data classification. Information Sciences, 417 . pp. 262-273. ISSN 0020-0255. (doi:10.1016/j.ins.2017.07.019) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:63941)

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Official URL:
https://doi.org/10.1016/j.ins.2017.07.019

Abstract

The orthogonal distance from an instance to the subspace of a class is a key metric for pattern classification by the class subspace-based methods. There is a close relationship between the orthogonal distance and the residual standard deviation of a test instance from the class subspace. In this paper, we shall show that an established and widely-used relationship, between the residual standard deviation and the sum of squares of the residual PC scores, is not precise, and thus can lead to incorrect results, for the inference of high-dimensional data which nowadays are common in practice.

Item Type: Article
DOI/Identification number: 10.1016/j.ins.2017.07.019
Uncontrolled keywords: ClassificationHigh-dimensional dataOrthogonal distancePrincipal component analysis (PCA)Soft independent modelling of class analogy (SIMCA)
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
Depositing User: R. Zhu
Date Deposited: 10 Oct 2017 20:41 UTC
Last Modified: 04 Mar 2024 15:41 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/63941 (The current URI for this page, for reference purposes)

University of Kent Author Information

Zhu, Rui.

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