Deep clustering methods improve the performance of clustering tasks by
jointly optimizing deep representation learning and clustering. While numerous
deep clustering algorithms have been proposed, most of them rely on
artificially constructed pseudo targets for performing clustering. This
construction process requires some prior knowledge, and it is challenging to
determine a suitable pseudo target for clustering. To address this issue, we
propose a deep embedding clustering algorithm driven by sample stability
(DECS), which eliminates the requirement of pseudo targets. Specifically, we
start by constructing the initial feature space with an autoencoder and then
learn the cluster-oriented embedding feature constrained by sample stability.
The sample stability aims to explore the deterministic relationship between
samples and all cluster centroids, pulling samples to their respective clusters
and keeping them away from other clusters with high determinacy. We analyzed
the convergence of the loss using Lipschitz continuity in theory, which
verifies the validity of the model. The experimental results on five datasets
illustrate that the proposed method achieves superior performance compared to
state-of-the-art clustering approaches.

通过样本稳定性驱动的深度嵌入聚类 (DECS) 算法，消除了对伪目标的需求，并在五个数据集上展示了超越现有聚类方法的优越性能。