An algorithm for clusterwise linear regression based on smoothing techniques

DOI10.1007/s11590-014-0749-3zbMath1311.90106OpenAlexW1966363216MaRDI QIDQ2017840

Julien Ugon, Hijran G. Mirzayeva, Adil M. Bagirov

Publication date: 23 March 2015

Published in: Optimization Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11590-014-0749-3

zbMATH Keywords

nonsmooth optimizationnonconvex optimizationsmoothing techniquesclusterwise regression

Mathematics Subject Classification ID

Nonconvex programming, global optimization (90C26)

Related Items (6)

A clusterwise nonlinear regression algorithm for interval-valued data ⋮ On Cluster-Aware Supervised Learning: Frameworks, Convergent Algorithms, and Applications ⋮ Clusterwise support vector linear regression ⋮ DC programming algorithm for clusterwise linear \(L_1\) regression ⋮ Incremental DC optimization algorithm for large-scale clusterwise linear regression ⋮ Nonsmooth DC programming approach to clusterwise linear regression: optimality conditions and algorithms

Uses Software

• UCI-ml
• Algorithm 39

Cites Work

• A maximum likelihood methodology for clusterwise linear regression
• Optimal covering of plane domains by circles via hyperbolic smoothing
• Clusterwise PLS regression on a stochastic process
• A fast algorithm for clusterwise linear regression
• Clusterwise linear regression
• Extensions to the repetitive branch and bound algorithm for globally optimal clusterwise regression
• Nonsmooth nonconvex optimization approach to clusterwise linear regression problems
• The hyperbolic smoothing clustering method
• Hyperbolic smoothing function method for minimax problems

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