Revisiting Sparsity Constraint Under High-Rank Property in Partial Multi-Label Learning

TL;DR

This paper challenges traditional assumptions in Partial Multi-Label Learning by showing sparsity constraints can lead to high-rank label matrices, and proposes a novel method to improve learning in real-world noisy scenarios.

Contribution

It introduces Schirn, a new approach that applies sparsity to noise labels and enforces high-rank properties on predicted labels, addressing limitations of previous methods.

Findings

Schirn outperforms state-of-the-art methods in experiments.

Sparsity constraints contribute to high-rank label matrices.

The method effectively handles real-world noisy multi-label data.

Abstract

Partial Multi-Label Learning (PML) extends the multi-label learning paradigm to scenarios where each sample is associated with a candidate label set containing both ground-truth labels and noisy labels. Existing PML methods commonly rely on two assumptions: sparsity of the noise label matrix and low-rankness of the ground-truth label matrix. However, these assumptions are inherently conflicting and impractical for real-world scenarios, where the true label matrix is typically full-rank or close to full-rank. To address these limitations, we demonstrate that the sparsity constraint contributes to the high-rank property of the predicted label matrix. Based on this, we propose a novel method Schirn, which introduces a sparsity constraint on the noise label matrix while enforcing a high-rank property on the predicted label matrix. Extensive experiments demonstrate the superior performance of Schirn compared to state-of-the-art methods, validating its effectiveness in tackling real-world PML challenges.