Semi-supervised learning with max-margin graph cuts

• URL: http://arxiv.org/abs/2604.26818v1
• Date: Wed, 29 Apr 2026 15:46:46 GMT
• Title: Semi-supervised learning with max-margin graph cuts
• Abstract summary: This paper proposes a novel algorithm for semisupervised learning.<n>This algorithm learns graph cuts that maximize the margin with respect to the labels induced by the harmonic function solution.<n>We motivate the approach, compare it to existing work, and prove a bound on its generalization error.
• Score: 31.380197551243807
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper proposes a novel algorithm for semisupervised learning. This algorithm learns graph cuts that maximize the margin with respect to the labels induced by the harmonic function solution. We motivate the approach, compare it to existing work, and prove a bound on its generalization error. The quality of our solutions is evaluated on a synthetic problem and three UCI ML repository datasets. In most cases, we outperform manifold regularization of support vector machines, which is a state-of-the-art approach to semi-supervised max-margin learning.

Related papers

• Towards An Unsupervised Learning Scheme for Efficiently Solving Parameterized Mixed-Integer Programs [6.1860817947800655]
  We train an autoencoder for binary variables in an unsupervised learning fashion.<n>We present a strategy to construct a class of cutting plane constraints from the decoder parameters of an offline-trained AE.<n>Their integration into the primal MIP problem leads to a tightened MIP with the reduced feasible region.
  arXiv  Detail & Related papers  (2024-12-23T14:48:32Z)
• Scalable Deep Metric Learning on Attributed Graphs [10.092560681589578]
  We develop a graph embedding method, which is based on extending deep metric and unbiased contrastive learning techniques. Based on a multi-classt loss function, we present two algorithms -- DMT for semi-supervised learning and DMAT-i for the unsupervised case.
  arXiv  Detail & Related papers  (2024-11-20T03:34:31Z)
• An Unsupervised Learning Framework Combined with Heuristics for the Maximum Minimal Cut Problem [5.092968949752308]
  This work proposes an unsupervised learning framework combined with Maximums for MMCP. A crucial observation is that each solution corresponds to at least one spanning tree. We conduct extensive experiments to evaluate our framework and give a specific application.
  arXiv  Detail & Related papers  (2024-08-16T02:07:34Z)
• M3C: A Framework towards Convergent, Flexible, and Unsupervised Learning of Mixture Graph Matching and Clustering [57.947071423091415]
  We introduce Minorize-Maximization Matching and Clustering (M3C), a learning-free algorithm that guarantees theoretical convergence. We develop UM3C, an unsupervised model that incorporates novel edge-wise affinity learning and pseudo label selection. Our method outperforms state-of-the-art graph matching and mixture graph matching and clustering approaches in both accuracy and efficiency.
  arXiv  Detail & Related papers  (2023-10-27T19:40:34Z)
• Semi-Supervised Laplace Learning on Stiefel Manifolds [48.3427853588646]
  We develop the framework Sequential Subspace for graph-based, supervised samples at low-label rates. We achieves that our methods at extremely low rates, and high label rates.
  arXiv  Detail & Related papers  (2023-07-31T20:19:36Z)
• Support Vector Machines with the Hard-Margin Loss: Optimal Training via Combinatorial Benders' Cuts [8.281391209717105]
  We show how to train the hard-margin SVM model to global optimality. We introduce an iterative sampling and sub decomposition algorithm that solves the problem.
  arXiv  Detail & Related papers  (2022-07-15T18:21:51Z)
• On a class of geodesically convex optimization problems solved via Euclidean MM methods [50.428784381385164]
  We show how a difference of Euclidean convexization functions can be written as a difference of different types of problems in statistics and machine learning. Ultimately, we helps the broader broader the broader the broader the broader the work.
  arXiv  Detail & Related papers  (2022-06-22T23:57:40Z)
• A Bi-Level Framework for Learning to Solve Combinatorial Optimization on Graphs [91.07247251502564]
  We propose a hybrid approach to combine the best of the two worlds, in which a bi-level framework is developed with an upper-level learning method to optimize the graph. Such a bi-level approach simplifies the learning on the original hard CO and can effectively mitigate the demand for model capacity.
  arXiv  Detail & Related papers  (2021-06-09T09:18:18Z)
• Individually Fair Gradient Boosting [86.1984206610373]
  We consider the task of enforcing individual fairness in gradient boosting. We show that our algorithm converges globally and generalizes. We also demonstrate the efficacy of our algorithm on three ML problems susceptible to algorithmic bias.
  arXiv  Detail & Related papers  (2021-03-31T03:06:57Z)
• Semi-Supervised Learning with Meta-Gradient [123.26748223837802]
  We propose a simple yet effective meta-learning algorithm in semi-supervised learning. We find that the proposed algorithm performs favorably against state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-07-08T08:48:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.