Self-Enhanced Density Clustering for High Dimension and Low Sample Size Data

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Abstract
Clustering on high-dimensional and low sample size (HDLSS) data remains a critical, persistent challenge where extreme sparsity and noise confound cluster analysis. This creates a dilemma: spectral methods fail as distance metrics degrade, while deep clustering tends to over-fit scarce data. To break this dilemma, a Self-Enhanced Density Clustering (SEDC) framework that integrates the cluster structure discovery and embedding representation learning into an iterative enhancement process is proposed in this paper. Specifically, SEDC uses adaptive density-derived centroids to parameterize probabilistic soft labels, which in turn supervise a lightweight multilayer perceptron (MLP) to learn the low-dimensional embedding from data. The resulting embedding provides a refined metric space for further generating superior labels in the subsequent interaction process. This feedback forms a mutual reinforcement that progressively enhances the discrimination of embedding while rigorously mitigating over-fitting. Extensive experiments on 43 challenging HDLSS datasets demonstrate state-of-the-art performance, substantially outperforming popular clustering methods. This work delivers a principled and promising solution for robust data clustering in HDLSS situations.
Funding Information
  • National Key Research and Development Program of China (2024YFE0202700)
  • National Natural Science Foundation of China (62576128&#x3b;62576178&#x3b;62576002&#x3b;62306171)
  • National Natural Science Foundation of China (U2433216)
  • Natural Science Foundation of Jiangsu Province (BK20231337)
  • Zhejiang Provincial Natural Science Foundation of China (LMS26F020035)

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