Pages that link to "Item:Q5348005"
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The following pages link to Modified Cheeger and ratio cut methods using the Ginzburg–Landau functional for classification of high-dimensional data (Q5348005):
Displaying 11 items.
- An MBO scheme for minimizing the graph Ohta-Kawasaki functional (Q2022733) (← links)
- Data clustering based on the modified relaxation Cheeger cut model (Q2115038) (← links)
- Multi-class transductive learning based on \(\ell^1\) relaxations of Cheeger cut and Mumford-Shah-Potts model (Q2251265) (← links)
- Comparisons of different methods for balanced data classification under the discrete non-local total variational framework (Q2668555) (← links)
- Diffuse interface models on graphs for classification of high dimensional data (Q2805269) (← links)
- The 1-Laplacian Cheeger Cut: Theory and Algorithms (Q2992631) (← links)
- Graph-based optimization approaches for machine learning, uncertainty quantification and networks (Q3295563) (← links)
- Preface for <i>Inverse Problems</i> special issue on learning and inverse problems (Q5348002) (← links)
- Preconditioned Algorithm for Difference of Convex Functions with Applications to Graph Ginzburg–Landau Model (Q6088330) (← links)
- Generalized nonconvex hyperspectral anomaly detection via background representation learning with dictionary constraint (Q6556793) (← links)
- An efficient and versatile variational method for high-dimensional data classification (Q6604513) (← links)
