Learning with Proper Partial Labels
- URL: http://arxiv.org/abs/2112.12303v1
- Date: Thu, 23 Dec 2021 01:37:03 GMT
- Title: Learning with Proper Partial Labels
- Authors: Zhenguo Wu, Masashi Sugiyama
- Abstract summary: Partial-label learning is a kind of weakly-supervised learning with inexact labels.
We show that this proper partial-label learning framework includes many previous partial-label learning settings.
We then derive a unified unbiased estimator of the classification risk.
- Score: 87.65718705642819
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Partial-label learning is a kind of weakly-supervised learning with inexact
labels, where for each training example, we are given a set of candidate labels
instead of only one true label. Recently, various approaches on partial-label
learning have been proposed under different generation models of candidate
label sets. However, these methods require relatively strong distributional
assumptions on the generation models. When the assumptions do not hold, the
performance of the methods is not guaranteed theoretically. In this paper, we
propose the notion of properness on partial labels. We show that this proper
partial-label learning framework includes many previous partial-label learning
settings as special cases. We then derive a unified unbiased estimator of the
classification risk. We prove that our estimator is risk-consistent by
obtaining its estimation error bound. Finally, we validate the effectiveness of
our algorithm through experiments.
Related papers
- Learning with Complementary Labels Revisited: The Selected-Completely-at-Random Setting Is More Practical [66.57396042747706]
Complementary-label learning is a weakly supervised learning problem.
We propose a consistent approach that does not rely on the uniform distribution assumption.
We find that complementary-label learning can be expressed as a set of negative-unlabeled binary classification problems.
arXiv Detail & Related papers (2023-11-27T02:59:17Z) - Partial-Label Regression [54.74984751371617]
Partial-label learning is a weakly supervised learning setting that allows each training example to be annotated with a set of candidate labels.
Previous studies on partial-label learning only focused on the classification setting where candidate labels are all discrete.
In this paper, we provide the first attempt to investigate partial-label regression, where each training example is annotated with a set of real-valued candidate labels.
arXiv Detail & Related papers (2023-06-15T09:02:24Z) - Class-Distribution-Aware Pseudo Labeling for Semi-Supervised Multi-Label
Learning [97.88458953075205]
Pseudo-labeling has emerged as a popular and effective approach for utilizing unlabeled data.
This paper proposes a novel solution called Class-Aware Pseudo-Labeling (CAP) that performs pseudo-labeling in a class-aware manner.
arXiv Detail & Related papers (2023-05-04T12:52:18Z) - Dist-PU: Positive-Unlabeled Learning from a Label Distribution
Perspective [89.5370481649529]
We propose a label distribution perspective for PU learning in this paper.
Motivated by this, we propose to pursue the label distribution consistency between predicted and ground-truth label distributions.
Experiments on three benchmark datasets validate the effectiveness of the proposed method.
arXiv Detail & Related papers (2022-12-06T07:38:29Z) - Learning from Multiple Unlabeled Datasets with Partial Risk
Regularization [80.54710259664698]
In this paper, we aim to learn an accurate classifier without any class labels.
We first derive an unbiased estimator of the classification risk that can be estimated from the given unlabeled sets.
We then find that the classifier obtained as such tends to cause overfitting as its empirical risks go negative during training.
Experiments demonstrate that our method effectively mitigates overfitting and outperforms state-of-the-art methods for learning from multiple unlabeled sets.
arXiv Detail & Related papers (2022-07-04T16:22:44Z) - Multi-class Probabilistic Bounds for Self-learning [13.875239300089861]
Pseudo-labeling is prone to error and runs the risk of adding noisy labels into unlabeled training data.
We present a probabilistic framework for analyzing self-learning in the multi-class classification scenario with partially labeled data.
arXiv Detail & Related papers (2021-09-29T13:57:37Z)
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.