Revisiting Sparsity Constraint Under High-Rank Property in Partial Multi-Label Learning
This addresses the challenge of handling noisy labels in multi-label learning for real-world applications where label matrices are high-rank, offering an incremental improvement over prior methods.
The paper tackles the problem of conflicting assumptions in Partial Multi-Label Learning (PML), where existing methods assume sparsity of noise and low-rankness of ground-truth labels, which are impractical for real-world high-rank scenarios. It proposes Schirn, a method that enforces sparsity on noise and high-rank on predicted labels, achieving superior performance over state-of-the-art methods in experiments.
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.