论文标题
从部分有效的数据中恢复准确的标记信息，以进行有效的多标签学习
Recovering Accurate Labeling Information from Partially Valid Data for Effective Multi-Label Learning
论文作者
论文摘要
部分多标签学习（PML）旨在通过嘈杂的监督从数据集诱导多标签预测指标，在该数据集中，每个培训实例都与多个候选标签相关联，但仅部分有效。为了解决嘈杂的问题，现有的PML方法基本上通过利用候选标签的地面信心来恢复地面真相标签，\ ie候选标签的可能性是基本真相。但是，他们忽略了非候选标签的信息，这有可能有助于地面标签恢复。在本文中，我们提议从标签富集中恢复地面真相标签，\ ie估计地面真相的信心，该标签由候选标签的相关程度和非偏置标签的相关程度组成。通过观察，我们进一步开发了一种新颖的两阶段PML方法，即\ emph {\下划线{p}人工\下划线{m} ulti-\ usew lisepline {l} abel \ abel \ underline {l}用\ useverline {l} abel {l} abel \ abselline {e} nline {e} nline {第一阶段，它估计具有不受限制的标签传播的标签富集，然后共同学习了富集标签的置信度和多标签预测指标。实验结果验证了\婴儿的表现胜过最先进的PML方法。
Partial Multi-label Learning (PML) aims to induce the multi-label predictor from datasets with noisy supervision, where each training instance is associated with several candidate labels but only partially valid. To address the noisy issue, the existing PML methods basically recover the ground-truth labels by leveraging the ground-truth confidence of the candidate label, \ie the likelihood of a candidate label being a ground-truth one. However, they neglect the information from non-candidate labels, which potentially contributes to the ground-truth label recovery. In this paper, we propose to recover the ground-truth labels, \ie estimating the ground-truth confidences, from the label enrichment, composed of the relevance degrees of candidate labels and irrelevance degrees of non-candidate labels. Upon this observation, we further develop a novel two-stage PML method, namely \emph{\underline{P}artial \underline{M}ulti-\underline{L}abel \underline{L}earning with \underline{L}abel \underline{E}nrichment-\underline{R}ecovery} (\baby), where in the first stage, it estimates the label enrichment with unconstrained label propagation, then jointly learns the ground-truth confidence and multi-label predictor given the label enrichment. Experimental results validate that \baby outperforms the state-of-the-art PML methods.
