Learning from Noisy Label Distributions

TL;DR

This paper introduces a probabilistic model for learning classifiers from noisy label distributions at the group level, effectively estimating true labels and improving classification accuracy despite label noise.

Contribution

It proposes a novel variational Bayesian approach to handle noisy label distributions and accurately estimate true labels, advancing learning from group-level noisy data.

Findings

Outperforms existing methods in true label estimation

Effective in handling unknown label noise

Demonstrates robustness in numerical experiments

Abstract

In this paper, we consider a novel machine learning problem, that is, learning a classifier from noisy label distributions. In this problem, each instance with a feature vector belongs to at least one group. Then, instead of the true label of each instance, we observe the label distribution of the instances associated with a group, where the label distribution is distorted by an unknown noise. Our goals are to (1) estimate the true label of each instance, and (2) learn a classifier that predicts the true label of a new instance. We propose a probabilistic model that considers true label distributions of groups and parameters that represent the noise as hidden variables. The model can be learned based on a variational Bayesian method. In numerical experiments, we show that the proposed model outperforms existing methods in terms of the estimation of the true labels of instances.