Constrained nonnegative matrix factorization-based semi-supervised multilabel learning

In many multilabel learning applications, instances with labels being fully provided are scarce, while partially labelled data and unlabelled data are more common due to the expensive cost of manual labelling. However, most of existing models are based on the assumption that the fully labelled training data is sufficient. To deal with the partially labelled and unlabelled data effectively, we present a novel semi-supervised multilabel learning approach based on constrained non-negative matrix factorization in this paper. This approach assumes that if two instances are highly similar in terms of their features, they would also be similar in their associated labels set. Specifically, We first define three matrices to measure the similarity of each pair of instances in two different ways. Then, the optimal assignation of labels to the unlabelled instance is determined by minimizing the differentiation between these two similarity sets via a non-negative matrix factorization process. We also present a threshold learning algorithm to determine the classification threshold for each label in our proposed approach. Extensive experiment is conducted on various datasets, and the results demonstrate that our method show significantly better performance than other state-of-the-art approaches. It is especially suitable for the situations with a smaller size of labelled training data, or subset of the training data are partially labelled.