SC-VAEGAN: spectral-constrained variational autoencoder with generative adversarial networks for robust unsupervised deep clustering with density-aware latent representations

Abstract

This paper introduced a principled and empirically effective deep unsupervised clustering framework, called SC-VAEGAN, which integrates variational generative modeling, adversarial latent space regularization, spectral graph topological constraints, UMAP-guided initialization and contrastive auxiliary learning within a single end-to-end learnable objective. SC-VAEGAN is statistically validated using five heterogeneous datasets and extensively ablated to improve the state-of-the-art results, which are 5.0–6.0% higher than the best previous method on primary metrics. In addition to showing the application of these theoretical contributions to their particular problem, they give a general framework for future work on topology-preserving generative clustering. Although SC-VAEGAN has shown good empirical results, it has some limitations to be recognized. The k-NN graph is constructed with computational effort per batch of O(B²•d_z), where d_z is the dimensionality of the data. The approximation of nearest neighbor methods does help alleviate this computational burden. K (number of clusters) is a hyperparameter which should be specified in the model, with further work which could involve automatic determination of the cluster number through non-parametric methods still to be undertaken. Possible future directions include: (i) incorporating hyperbolic geometry for hierarchically structured data; (ii) extending to federated clustering with guarantees on differential privacy; and (iii) extending to dynamic cluster number estimation using sequential hypothesis testing on eigenspectrum gaps.