A deep support vector clustering algorithm for unsupervised and semi‐supervised learning
Zhen Zhang, Shuyan Chen, Xin Liu · Canadian Journal of Statistics · 2025
Abstract As a widely carried out task in data‐driven applications, clustering relies on good data representation. Since deep neural networks are powerful tools for the analysis of clustering‐friendly representations, certain combinations of clustering and deep models have been explored in the literature. Yet, only limited improvement has been achieved for real data with complex structures such as positive and unlabelled (PU) data. In this article we propose an unsupervised clustering model, called the deep support vector clustering (dSVC). The method combines a deep autoencoder neural network with hinge loss, and is further extended to binary semi‐supervised PU data learning. Theoretical results are established for label recovery and novelty detection using a large‐margin classifier. Intensive numerical experiments on multiple datasets of both high and low dimension validate the efficiency of the proposed approach. We found that the proposed approach constructs clusters in a manner opposite to the popular generative adversarial network (GAN) model.