# Partial Label Learning with Self-Guided Retraining

## TL;DR

This paper introduces a novel self-training approach for partial label learning, utilizing maximum infinity norm regularization and quadratic programming to improve label disambiguation and outperform existing methods.

## Contribution

It presents the first self-training framework for partial label learning with a convex-concave formulation and efficient optimization via quadratic programming.

## Key findings

- Significantly outperforms state-of-the-art methods on various datasets.
- Effectively automates pseudo-labeling without manual thresholding.
- Demonstrates robustness on both synthesized and real-world data.

## Abstract

Partial label learning deals with the problem where each training instance is assigned a set of candidate labels, only one of which is correct. This paper provides the first attempt to leverage the idea of self-training for dealing with partially labeled examples. Specifically, we propose a unified formulation with proper constraints to train the desired model and perform pseudo-labeling jointly. For pseudo-labeling, unlike traditional self-training that manually differentiates the ground-truth label with enough high confidence, we introduce the maximum infinity norm regularization on the modeling outputs to automatically achieve this consideratum, which results in a convex-concave optimization problem. We show that optimizing this convex-concave problem is equivalent to solving a set of quadratic programming (QP) problems. By proposing an upper-bound surrogate objective function, we turn to solving only one QP problem for improving the optimization efficiency. Extensive experiments on synthesized and real-world datasets demonstrate that the proposed approach significantly outperforms the state-of-the-art partial label learning approaches.