Image Clustering With Transition Probabilities Learning

Large-scale multi-view clustering for image data has achieved impressive clustering performance and efficiency. However, most methods lack interpretability in clustering and do not fully consider the complementarity of distributions between different views. To address these problems, we introduce Multi-View Clustering with Transition Probabilities Learning (MVC-TPL). Specifically, we construct an anchor graph factorization model from the perspective of transition probabilities, while simultaneously learning transition probability matrices from samples to clusters and from anchor points to clusters, serving as soft label matrices for samples and anchor points, respectively. This model enables one-step label acquisition and provides the model with a sound probability interpretation. Moreover, since the clusters of samples and anchor points should be consistent across all views, we employ Schatten p-norm regularization on the two matrices, effectively mining the complementary information distributed among the views, thereby aligning the labels across views more consistently. Comprehensive testing on four small-scale datasets and three large-scale datasets confirms the effectiveness of this model.