Cohesive Pair-Wises Constrained Deep Embedding for Semi-Supervised Clustering with Very Few Labeled Samples*

Semi-supervised learning is a powerful paradigm for excavating latent structures of between labeled and unlabeled samples under the view of models constructing. Currently, graph-based models solve the approximate matrix that directly represent distributions of samples by the spatial metric. The crux lies in optimizing connections of samples, which is achieved by subjecting to must-links or cannot-links. Unfortunately, to find links are rather difficult for semi-supervised clustering with very few labeled samples, therefore, significantly impairs the robustness and accuracy in such scenario. To address this problem, we propose the Cohesive Pair-wises Constrained deep Embedding model (CPCE) to obtain an optimal embedding for representing the category distribution of samples and avoid the failed graph-structure of the global samples. CPCE designs the deep network framework based on CNN-Autoencoder by minimizing reconstruct errors of samples, and build up constrains both of the sample distribution for within-class and the category distribution for intra-class to optimal the latent embedding. Then, leverage the strong supervised information obtained from cohesive pair-wises to pull samples into within-class, which avoid the similarity of high-dimension features located in different categories to achieve more the compact solution. We demonstrate the proposed method in popular datasets and compare the superiority with popular methods