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

DOI10.1016/j.ins.2018.12.061zbMath1443.62190OpenAlexW2906680751WikidataQ128673597 ScholiaQ128673597MaRDI QIDQ2005504

Rui Zhu, Jing-Hao Xue, Mingzhi Dong

Publication date: 8 October 2020

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

Full work available at URL: https://openaccess.city.ac.uk/id/eprint/21194/1/LD2S-IS-R2.pdf

zbMATH Keywords

distance metric learningorthogonal distancedistance to subspacenearest subspace methods (NSM)score distance

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items (2)

Robust subspace clustering based on non-convex low-rank approximation and adaptive kernel ⋮ A fast diagonal distance metric learning approach for large-scale datasets

Uses Software

• LMNN

Cites Work

• Unnamed Item
• On the orthogonal distance to class subspaces for high-dimensional data classification
• Pattern recognition by means of disjoint principal components models
• Ordinal margin metric learning and its extension for cross-distribution image data
• On selecting interacting features from high-dimensional data
• Nonparametric functional data analysis. Theory and practice.
• Median-Based Classifiers for High-Dimensional Data
• Incorporating prior probabilities into high-dimensional classifiers

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