Progressive Identification of True Labels for Partial-Label Learning

TL;DR

This paper introduces a flexible, model-agnostic framework for partial-label learning that uses a progressive label identification method, improving scalability and achieving state-of-the-art results.

Contribution

It proposes a novel risk estimator, a theoretical analysis of classifier consistency, and a progressive identification algorithm that enhances scalability and flexibility in PLL.

Findings

Achieves state-of-the-art performance on benchmark datasets.

The proposed method is model-independent and loss-independent.

Demonstrates effective label identification and risk minimization.

Abstract

Partial-label learning (PLL) is a typical weakly supervised learning problem, where each training instance is equipped with a set of candidate labels among which only one is the true label. Most existing methods elaborately designed learning objectives as constrained optimizations that must be solved in specific manners, making their computational complexity a bottleneck for scaling up to big data. The goal of this paper is to propose a novel framework of PLL with flexibility on the model and optimization algorithm. More specifically, we propose a novel estimator of the classification risk, theoretically analyze the classifier-consistency, and establish an estimation error bound. Then we propose a progressive identification algorithm for approximately minimizing the proposed risk estimator, where the update of the model and identification of true labels are conducted in a seamless manner. The resulting algorithm is model-independent and loss-independent, and compatible with stochastic optimization. Thorough experiments demonstrate it sets the new state of the art.