Modified Cheeger and ratio cut methods using the Ginzburg–Landau functional for classification of high-dimensional data

DOI10.1088/1361-6420/33/7/074003zbMath1409.68239OpenAlexW2619808280MaRDI QIDQ5348005

Xiaoran Yan, Kristina Lerman, Ekaterina Merkurjev, Andrea L. Bertozzi

Publication date: 11 August 2017

Published in: Inverse Problems (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1088/1361-6420/33/7/074003

zbMATH Keywords

classificationtotal variationgraphsGinzburg-Landau functionalspectral clusteringratio cutCheeger cut

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05) Graphs and linear algebra (matrices, eigenvalues, etc.) (05C50) Numerical solution to inverse problems in abstract spaces (65J22)

Related Items

Comparisons of different methods for balanced data classification under the discrete non-local total variational framework, Preface for Inverse Problems special issue on learning and inverse problems, Preconditioned Algorithm for Difference of Convex Functions with Applications to Graph Ginzburg–Landau Model, An MBO scheme for minimizing the graph Ohta-Kawasaki functional, Diffuse Interface Models on Graphs for Classification of High Dimensional Data, Data clustering based on the modified relaxation Cheeger cut model

Uses Software

• MNIST

Cites Work

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