Robust multi-label semi-supervised classification

Abstract

The lack of labels and the poor quality of data present a common challenge in many data mining and machine learning problems. The model performance might be limited if only a few labeled samples are available for training. Moreover, the data may be noisy in reality, which disturbs the data distribution and further hinders the learning performance. These problems become even more critical in multi-label classification, which has an intricate label space and usually requires clean data for training. In this paper, we aim to tackle the above problems by learning effective feature representations for semi-supervised multi-label classification. We propose a novel approach named Adaptive Low-rank Semi-supervised learning for Multi-label classification (ALSM). It learns an intermediate feature space for both labeled and unlabeled training samples via low-rank matrix recovery, and employs an adaptive semi-supervised learning strategy to train a multi-label classifier. We solve the problem by devising an optimization algorithm based on the Alternating Direction Method of Multipliers (ADMM). Our approach can be applied to both transductive and inductive semi-supervised multi-label classification problems. Experiments on five benchmark multi-label datasets show that our approach outperforms the representative multi-label classification methods in most cases.