Deep Embedding Clustering Driven by Sample Stability
Zhanwen Cheng Feijiang Li Jieting Wang Yuhua Qian
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.