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

• URL: http://arxiv.org/abs/2505.20938v1
• Date: Tue, 27 May 2025 09:25:50 GMT
• Title: Revisiting Sparsity Constraint Under High-Rank Property in Partial Multi-Label Learning
• Abstract summary: Partial Multi-Label Learning (PML) extends the multi-label learning paradigm to scenarios where each sample is associated with a candidate label set.<n>Existing PML methods rely on two assumptions: sparsity of the noise label matrix and low-rankness of the ground-truth label matrix.<n>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.
• Score: 48.528799044535155
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Feature-Label Modal Alignment for Robust Partial Multi-Label Learning [66.1611192892514]
  We propose a novel PML method based on feature-label modal alignment (PML-MA)<n>PML-MA treats features and labels as two complementary modalities and restores their consistency through systematic alignment.<n>Experiments on both real-world and synthetic datasets demonstrate that PML-MA significantly outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2026-04-10T07:44:46Z)
• Rethinking Consistent Multi-Label Classification under Inexact Supervision [60.79309683889278]
  In partial multi-label learning, each instance is annotated with a candidate label set, among which only some labels are relevant.<n>In complementary multi-label learning, each instance is annotated with complementary labels indicating the classes to which the instance does not belong.
  arXiv  Detail & Related papers  (2025-10-05T08:30:32Z)
• Noise-Resistant Label Reconstruction Feature Selection for Partial Multi-Label Learning [3.635311806373203]
  "Curse of dimensionality" is prevalent across various data patterns, which increases the risk of model overfitting and leads to a decline in model classification performance.<n>Existing Partial Multi-label Learning (PML) methods addressing this problem are mainly based on the low-rank assumption.<n>In this paper, a PML feature selection method is proposed considering two important characteristics of dataset.
  arXiv  Detail & Related papers  (2025-06-05T06:31:04Z)
• Mixed Blessing: Class-Wise Embedding guided Instance-Dependent Partial Label Learning [53.64180787439527]
  In partial label learning (PLL), every sample is associated with a candidate label set comprising the ground-truth label and several noisy labels.<n>For the first time, we create class-wise embeddings for each sample, which allow us to explore the relationship of instance-dependent noisy labels.<n>To reduce the high label ambiguity, we introduce the concept of class prototypes containing global feature information.
  arXiv  Detail & Related papers  (2024-12-06T13:25:39Z)
• Dual-Decoupling Learning and Metric-Adaptive Thresholding for Semi-Supervised Multi-Label Learning [81.83013974171364]
  Semi-supervised multi-label learning (SSMLL) is a powerful framework for leveraging unlabeled data to reduce the expensive cost of collecting precise multi-label annotations.<n>Unlike semi-supervised learning, one cannot select the most probable label as the pseudo-label in SSMLL due to multiple semantics contained in an instance.<n>We propose a dual-perspective method to generate high-quality pseudo-labels.
  arXiv  Detail & Related papers  (2024-07-26T09:33:53Z)
• Multi-Label Noise Transition Matrix Estimation with Label Correlations: Theory and Algorithm [73.94839250910977]
  Noisy multi-label learning has garnered increasing attention due to the challenges posed by collecting large-scale accurate labels. The introduction of transition matrices can help model multi-label noise and enable the development of statistically consistent algorithms. We propose a novel estimator that leverages label correlations without the need for anchor points or precise fitting of noisy class posteriors.
  arXiv  Detail & Related papers  (2023-09-22T08:35:38Z)
• Deep Partial Multi-Label Learning with Graph Disambiguation [27.908565535292723]
  We propose a novel deep Partial multi-Label model with grAph-disambIguatioN (PLAIN) Specifically, we introduce the instance-level and label-level similarities to recover label confidences. At each training epoch, labels are propagated on the instance and label graphs to produce relatively accurate pseudo-labels.
  arXiv  Detail & Related papers  (2023-05-10T04:02:08Z)
• Class-Distribution-Aware Pseudo Labeling for Semi-Supervised Multi-Label Learning [97.88458953075205]
  Pseudo-labeling has emerged as a popular and effective approach for utilizing unlabeled data. This paper proposes a novel solution called Class-Aware Pseudo-Labeling (CAP) that performs pseudo-labeling in a class-aware manner.
  arXiv  Detail & Related papers  (2023-05-04T12:52:18Z)
• One Positive Label is Sufficient: Single-Positive Multi-Label Learning with Label Enhancement [71.9401831465908]
  We investigate single-positive multi-label learning (SPMLL) where each example is annotated with only one relevant label. A novel method named proposed, i.e., Single-positive MultI-label learning with Label Enhancement, is proposed. Experiments on benchmark datasets validate the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2022-06-01T14:26:30Z)
• Recovering Accurate Labeling Information from Partially Valid Data for Effective Multi-Label Learning [23.665227794132566]
  Partial Multi-label Learning (PML) aims to induce the multi-label predictor from datasets with noisy supervision. We develop a novel two-stage PML method, namely emphunderlinePartial underlineMulti-underlineLabel underlineLabel, where in the first stage, it estimates the label enrichment with unconstrained label propagation. Experimental results validate that baby outperforms the state-of-the-art PML methods.
  arXiv  Detail & Related papers  (2020-06-20T04:13:24Z)
• Partial Multi-label Learning with Label and Feature Collaboration [21.294791188490056]
  Partial multi-label learning (PML) models the scenario where each training instance is annotated with a set of candidate labels. To achieve a credible predictor on PML data, we propose PML-LFC (Partial Multi-label Learning with Label and Feature Collaboration) PML-LFC estimates the confidence values of relevant labels for each instance using the similarity from both the label and feature spaces.
  arXiv  Detail & Related papers  (2020-03-17T08:34:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.