Consistency regularization for deep semi-supervised clustering with pairwise constraints

Due to its powerful learning capabilities for high-dimensional and complex data, deep semi-supervised clustering algorithms often outperform traditional semi-supervised clustering methods. However, most deep semi-supervised clustering methods cannot fully utilize prior knowledge and unlabeled data. Deep semi-supervised classification algorithms have recently made significant progress in using unlabeled data during training by combining a consistency regularization method. Consistency training encourages network predictions to remain consistent when the input is perturbed. Motivated by the success of consistency regularization methods, we proposed a new semi-supervised clustering framework based on Siamese networks. To leverage the additional structure of unlabeled data and to uncover more information hidden by pairwise constraints, we add a consistency regularization loss, calculated on unlabeled data and pairwise constraints, to our objective function. After consistency training, the connected data can be closer in the learned feature space, while the disconnected data can be far away. To verify the effectiveness of the proposed method, we conducted extensive experiments on several real-world data sets. Experimental results show that the proposed method is more effective than other state-of-the-art methods in clustering performance.