Semi-supervised label enhancement via structured semantic extraction

Label enhancement (LE) is a process of recovering the label distribution from logical labels in the datasets, the goal of which is to better express the label ambiguity through the form of label distribution. Existing LE work mainly focus on exploring the data distribution in the feature space based on complete features and complete logical labels. However, it is not always easy to obtain multi-label datasets with logical labels for all samples in real world, most of datasets have only a few samples with annotated labels. To this end, we propose a novel semi-supervised label enhancement method via structured semantic extraction (SLE-SSE), which can recover the complete label distribution from only a few logical labels. Firstly, we extract self-semantic of samples by expressing inherent ambiguity of each sample in the input space appropriately, and fill in the missing labels based on this kind of information. Secondly, we take advantage of low rank representation to extract the inter-semantics of between samples and between labels, respectively. Finally, we apply a simple but effective linear model to recover the complete label distribution by utilizing the structured semantic information including intra-sample, inter-sample and inter-label based information. Extensive comparative experiments validate the effectiveness of the proposed method.