通过特征空间结构提升部分多标签数据的标签消歧能力。
Reconsidering Feature Structure Information and Latent Space Alignment in Partial Multi-label Feature Selection
- 利用特征空间结构信息，在潜在空间中对齐标签与特征以消歧。
- 新方法在保留特征一致性的同时显著提升正类标签识别率。
- 适合处理标签模糊、噪声集中于标签空间的多标签场景。
部分多标签特征选择的目标是选取最具代表性的特征子集，其数据来源于存在标签模糊问题的部分多标签数据集。以往方法主要依赖标签内部信息及标签与特征间的关系进行标签消歧，却很少考虑特征空间中的信息，尤其在部分多标签场景下，通常认为噪声集中于标签空间而特征信息是可靠的。本文提出一种基于潜在空间对齐的方法，通过挖掘特征空间中的信息，利用标签与特征间的结构一致性，在潜在空间中实现标签消歧。此外，现有方法在收敛后高估了特征与标签在潜在空间中的一致性。为此，我们综合考虑潜在空间投影与特征空间、标签空间的相似性，提出新的特征选择项。该方法显著提升了所选特征对正标签的识别能力。大量实验验证了所提方法的优越性。
原文摘要 · Abstract (English)
The purpose of partial multi-label feature selection is to select the most representative feature subset, where the data comes from partial multi-label datasets that have label ambiguity issues. For label disambiguation, previous methods mainly focus on utilizing the information inside the labels and the relationship between the labels and features. However, the information existing in the feature space is rarely considered, especially in partial multi-label scenarios where the noises is considered to be concentrated in the label space while the feature information is correct. This paper proposes a method based on latent space alignment, which uses the information mined in feature space to disambiguate in latent space through the structural consistency between labels and features. In addition, previous methods overestimate the consistency of features and labels in the latent space after convergence. We comprehensively consider the similarity of latent space projections to feature space and label space, and propose new feature selection term. This method also significantly improves the positive label identification ability of the selected features. Comprehensive experiments demonstrate the superiority of the proposed method.
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