An efficient method for clustered multi-metric learning

DOI10.1016/j.ins.2018.08.055zbMath1441.68212OpenAlexW2889341738WikidataQ60255503 ScholiaQ60255503MaRDI QIDQ2200672

Carlos Morell, Bac T. Nguyen, Francesc J. Ferri, Bernard De Baets

Publication date: 22 September 2020

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

Full work available at URL: https://doi.org/10.1016/j.ins.2018.08.055

zbMATH Keywords

convex optimizationnearest-neighbor classificationheterogeneously distributed datamulti-metric learning

Mathematics Subject Classification ID

Convex programming (90C25) Applications of mathematical programming (90C90) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (3)

A multi-birth metric learning framework based on binary constraints ⋮ \(\mathrm{OPM^2L}\): an optimal instance partition-based multi-metric learning method for heterogeneous dataset classification ⋮ A fast diagonal distance metric learning approach for large-scale datasets

Uses Software

• KEEL
• Pegasos
• AS 136
• LMNN

Cites Work

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• Algorithm AS 136: A K-Means Clustering Algorithm
• Geometry preserving multi-task metric learning
• Pegasos: primal estimated sub-gradient solver for SVM
• Computing a nearest symmetric positive semidefinite matrix
• Supervised distance metric learning through maximization of the Jeffrey divergence
• Joint distance and similarity measure learning based on triplet-based constraints
• Local discriminative distance metrics ensemble learning
• Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization
• Iterative Solution of Nonlinear Equations in Several Variables
• On the convergence of sequential minimization algorithms
• Convergence of a block coordinate descent method for nondifferentiable minimization

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