Self-Supervised Graph Embedding Clustering.

Abstract

Manifold learning and $K$-means are two powerful techniques for data analysis in the field of artificial intelligence. When used for label learning, a promising strategy is to combine them directly and optimize both models simultaneously. However, a significant drawback of this approach is that it represents a naive and crude integration, requiring the optimization of all variables in both models without achieving a truly essential combination. Additionally, it introduces an extra hyperparameter and cannot ensure cluster balance. These challenges motivate us to explore whether a meaningful integration can be developed for dimensionality reduction clustering. In this paper, we propose a novel self-supervised manifold clustering framework that reformulates the two models into a unified framework, eliminating the need for additional hyperparameters while achieving dimensionality reduction clustering. Specifically, by analyzing the relationship between $K$-means and manifold learning, we construct a meaningful low-dimensional manifold clustering model that directly produces the label matrix of the data. The label information is then used to guide the learning of the manifold structure, ensuring consistency between the manifold structure and the labels. Notably, we identify a valuable role of ${\ell _{2,p}}$-norm regularization in clustering: maximizing the ${\ell _{2,p}}$-norm naturally maintains class balance during clustering, and we provide a theoretical proof of this property. Extensive experimental results demonstrate the efficiency of our proposed model.