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

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 pa...

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Bibliographic Details
Published inInformation sciences Vol. 417; pp. 262 - 273
Main Authors Zhu, Rui, Xue, Jing-Hao
Format Journal Article
LanguageEnglish
Published Elsevier Inc 01.11.2017
Subjects
Classification
High-dimensional data
Orthogonal distance
Principal component analysis (PCA)
Soft independent modelling of class analogy (SIMCA)
High-dimensional data
Orthogonal distance
Soft independent modelling of class analogy (SIMCA)
Principal component analysis (PCA)
Classification
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Summary: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.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2017.07.019