Partial Multi-label Learning via Constraint Clustering

Multi-label learning (MLL) refers to a learning task where each instance is associated with a set of labels. However, in most real-world applications, the labeling process is very expensive and time consuming. Partially multi-label learning (PML) refers to MLL where only a part of the labels are correctly annotated and the rest are false positive labels. The main purpose of PML is to learn and predict unseen multi-label data with less annotation cost. To address the ambiguities in the label set, existing popular PML research attempts to extract the label confidence for each candidate label. These methods mainly perform disambiguation by considering the correlation among labels or/and features. However, in PML because of noisy labels, the true correlation among labels is corrupted. These methods can be easily misled by noisy false-positive labels. In this paper, we propose Partial Multi-Label learning method via Constraint Clustering (PML-CC) to address PML based on the underlying structure of data. PML-CC gradually extracts high-confidence labels and then uses them to extract the rest labels. To find the high-confidence labels, it solves PML as a clustering task while considering extracted information from previous steps as constraints. In each step, PML-CC updates the extracted labels and uses them to extract the other labels. Experimental results show that our method successfully tackles PML tasks and outperforms the state-of-the-art methods on artificial and real-world datasets.