Graph-Based Semi-Supervised Segregated Lipschitz Learning

• URL: http://arxiv.org/abs/2411.03273v1
• Date: Tue, 05 Nov 2024 17:16:56 GMT
• Title: Graph-Based Semi-Supervised Segregated Lipschitz Learning
• Authors: Farid Bozorgnia, Yassine Belkheiri, Abderrahim Elmoataz,
• Abstract summary: This paper presents an approach to semi-supervised learning for the classification of data using the Lipschitz Learning on graphs. We develop a graph-based semi-supervised learning framework that leverages the properties of the infinity Laplacian to propagate labels in a dataset where only a few samples are labeled.
• Score: 0.21847754147782888
• License:
• Abstract: This paper presents an approach to semi-supervised learning for the classification of data using the Lipschitz Learning on graphs. We develop a graph-based semi-supervised learning framework that leverages the properties of the infinity Laplacian to propagate labels in a dataset where only a few samples are labeled. By extending the theory of spatial segregation from the Laplace operator to the infinity Laplace operator, both in continuum and discrete settings, our approach provides a robust method for dealing with class imbalance, a common challenge in machine learning. Experimental validation on several benchmark datasets demonstrates that our method not only improves classification accuracy compared to existing methods but also ensures efficient label propagation in scenarios with limited labeled data.

Related papers

• Reducing Labeling Costs in Sentiment Analysis via Semi-Supervised Learning [0.0]
  This study explores label propagation in semi-supervised learning. We employ a transductive label propagation method based on the manifold assumption for text classification. By extending labels based on cosine proximity within a nearest neighbor graph from network embeddings, we combine unlabeled data into supervised learning.
  arXiv  Detail & Related papers  (2024-10-15T07:25:33Z)
• Continuous Contrastive Learning for Long-Tailed Semi-Supervised Recognition [50.61991746981703]
  Current state-of-the-art LTSSL approaches rely on high-quality pseudo-labels for large-scale unlabeled data. This paper introduces a novel probabilistic framework that unifies various recent proposals in long-tail learning. We introduce a continuous contrastive learning method, CCL, extending our framework to unlabeled data using reliable and smoothed pseudo-labels.
  arXiv  Detail & Related papers  (2024-10-08T15:06:10Z)
• Dual-Decoupling Learning and Metric-Adaptive Thresholding for Semi-Supervised Multi-Label Learning [81.83013974171364]
  Semi-supervised multi-label learning (SSMLL) is a powerful framework for leveraging unlabeled data to reduce the expensive cost of collecting precise multi-label annotations. Unlike semi-supervised learning, one cannot select the most probable label as the pseudo-label in SSMLL due to multiple semantics contained in an instance. We propose a dual-perspective method to generate high-quality pseudo-labels.
  arXiv  Detail & Related papers  (2024-07-26T09:33:53Z)
• Dense FixMatch: a simple semi-supervised learning method for pixel-wise prediction tasks [68.36996813591425]
  We propose Dense FixMatch, a simple method for online semi-supervised learning of dense and structured prediction tasks. We enable the application of FixMatch in semi-supervised learning problems beyond image classification by adding a matching operation on the pseudo-labels. Dense FixMatch significantly improves results compared to supervised learning using only labeled data, approaching its performance with 1/4 of the labeled samples.
  arXiv  Detail & Related papers  (2022-10-18T15:02:51Z)
• Is margin all you need? An extensive empirical study of active learning on tabular data [66.18464006872345]
  We analyze the performance of a variety of active learning algorithms on 69 real-world datasets from the OpenML-CC18 benchmark. Surprisingly, we find that the classical margin sampling technique matches or outperforms all others, including current state-of-art.
  arXiv  Detail & Related papers  (2022-10-07T21:18:24Z)
• An Embarrassingly Simple Approach to Semi-Supervised Few-Shot Learning [58.59343434538218]
  We propose a simple but quite effective approach to predict accurate negative pseudo-labels of unlabeled data from an indirect learning perspective. Our approach can be implemented in just few lines of code by only using off-the-shelf operations.
  arXiv  Detail & Related papers  (2022-09-28T02:11:34Z)
• Learning with Neighbor Consistency for Noisy Labels [69.83857578836769]
  We present a method for learning from noisy labels that leverages similarities between training examples in feature space. We evaluate our method on datasets evaluating both synthetic (CIFAR-10, CIFAR-100) and realistic (mini-WebVision, Clothing1M, mini-ImageNet-Red) noise.
  arXiv  Detail & Related papers  (2022-02-04T15:46:27Z)
• Model-Change Active Learning in Graph-Based Semi-Supervised Learning [7.208515071018781]
  "Model-change" active learning quantifies the resulting change incurred in the classifier by introducing the additional label(s) We consider a family of convex loss functions for which the acquisition function can be efficiently approximated using the Laplace approximation of the posterior distribution.
  arXiv  Detail & Related papers  (2021-10-14T21:47:10Z)
• Re-distributing Biased Pseudo Labels for Semi-supervised Semantic Segmentation: A Baseline Investigation [30.688753736660725]
  We present a simple and yet effective Distribution Alignment and Random Sampling (DARS) method to produce unbiased pseudo labels. Our method performs favorably in comparison with state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-07-23T14:45:14Z)
• Ensemble Learning with Manifold-Based Data Splitting for Noisy Label Correction [20.401661156102897]
  noisy labels in training data can significantly degrade a model's generalization performance. We propose an ensemble learning method to correct noisy labels by exploiting the local structures of feature manifold. Our experiments on real-world noisy label datasets demonstrate the superiority of the proposed method over existing state-of-the-arts.
  arXiv  Detail & Related papers  (2021-03-13T07:24:58Z)
• Deep Active Learning for Biased Datasets via Fisher Kernel Self-Supervision [5.352699766206807]
  Active learning (AL) aims to minimize labeling efforts for data-demanding deep neural networks (DNNs) We propose a low-complexity method for feature density matching using self-supervised Fisher kernel (FK) Our method outperforms state-of-the-art methods on MNIST, SVHN, and ImageNet classification while requiring only 1/10th of processing.
  arXiv  Detail & Related papers  (2020-03-01T03:56:32Z)

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.