Noise-Resistant Label Reconstruction Feature Selection for Partial Multi-Label Learning

TL;DR

This paper introduces a noise-resistant feature selection method for Partial Multi-label Learning that effectively disambiguates labels and improves identification of positive labels, addressing high-dimensionality and label noise issues.

Contribution

It proposes a novel feature selection approach considering label noise resistance and connectivity, overcoming low-rank assumption limitations in PML.

Findings

Outperforms existing PML methods on benchmark datasets

Effectively disambiguates labels in noisy environments

Enhances feature selection accuracy for positive labels

Abstract

The "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. However, few studies have focused on this issue in Partial Multi-label Learning (PML), where each sample is associated with a set of candidate labels, at least one of which is correct. Existing PML methods addressing this problem are mainly based on the low-rank assumption. However, low-rank assumption is difficult to be satisfied in practical situations and may lead to loss of high-dimensional information. Furthermore, we find that existing methods have poor ability to identify positive labels, which is important in real-world scenarios. In this paper, a PML feature selection method is proposed considering two important characteristics of dataset: label relationship's noise-resistance and label connectivity. Our proposed method utilizes label relationship's noise-resistance to disambiguate labels. Then the learning process is designed through the reformed low-rank assumption. Finally, representative labels are found through label connectivity, and the weight matrix is reconstructed to select features with strong identification ability to these labels. The experimental results on benchmark datasets demonstrate the superiority of the proposed method.