Local-to-Global Deep Clustering on Approximate Uniform Manifold

Deep clustering usually treats the clustering assignments as supervisory signals to learn a more compact representation with deep neural networks, under the guidance of clustering-oriented losses. Nevertheless, we observe that, without reliable supervision, such losses for global clustering would destroy the locally geometric structure underlying data. In this paper, we propose a local-to-global deep clustering method based on approximate uniform manifold (LGC-AUM) to address this issue in a two-stage fashion. In the local stage, an intra-manifold preservation loss is proposed to preserve intra-manifold structures locally on basis of approximate uniform manifold, and an inter-manifold discrimination loss is for global inter-manifold structure. Thus, this stage serves to learn more discriminative structure-preserving features by reducing the correlations between different manifolds, which paves the way for the final clustering. Build off the learned features, the second stage explores a clustering loss based on approximate uniform manifold to establish stable network training for effective clustering with two auxiliary distributions. Experiments on five benchmark datasets verify the efficacy of our LGC-AUM as compared to several well-behaved clustering counterparts.