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

DOI10.1016/J.INS.2017.07.019zbMath1435.62257OpenAlexW2735051402MaRDI QIDQ778457

Jing-Hao Xue, Rui Zhu

Publication date: 2 July 2020

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: http://openaccess.city.ac.uk/id/eprint/20734/1/RuiZhu-INS-2017-UCL.pdf

zbMATH Keywords

classificationhigh-dimensional dataprincipal component analysis (PCA)orthogonal distancesoft independent modelling of class analogy (SIMCA)

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items (2)

Joint correntropy metric weighting and block diagonal regularizer for robust multiple kernel subspace clustering ⋮ Learning distance to subspace for the nearest subspace methods in high-dimensional data classification

Uses Software

• chemCal

Cites Work

• Fast semi-supervised clustering with enhanced spectral embedding
• Pattern recognition by means of disjoint principal components models
• Global discriminative-based nonnegative spectral clustering
• Nonparametric functional data analysis. Theory and practice.

This page was built for publication: On the orthogonal distance to class subspaces for high-dimensional data classification