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.

As data sources become ever more numerous, classification for multi-view data represented by heterogeneous features has been involved in many data mining applications. Most existing methods either directly concatenate all views or separately tackle each view, neglecting the correlation and diversity among views. Moreover, they often encounter an extra hyper-parameter that needs to be manually tuned, degenerating the applicability of models. In this paper, we present a robust supervised learning framework for multi-view classification, seeking a better representation and fusion of multiple views. Specifically, our framework discriminates different views with adaptively optimized view-wise weight factors and coalesces them to learn a joint projection subspace compatible across multiple views in an adaptive-weighting manner, thereby avoiding the intractable hyper-parameter. Meanwhile, the consensus and complementary information of original views can be naturally integrated into the learned subspace, in turn enhancing the discrimination of the subspace for subsequent classification. An efficient convergent algorithm is developed to iteratively optimize the formulated framework. Experiments on real datasets demonstrate the effectiveness and superiority of the proposed method.

Clustering is inherently a difficult task, and is made even more difficult when the selection of relevant features is also an issue. In this paper we propose an approach for simultaneous clustering and feature selection using a niching memetic algorithm. Our approach (which we call NMA_CFS) makes feature selection an integral part of the global clustering search procedure and attempts to overcome the problem of identifying less promising locally optimal solutions in both clustering and feature selection, without making any a priori assumption about the number of clusters. Within the NMA_CFS procedure, a variable composite representation is devised to encode both feature selection and cluster centers with different numbers of clusters. Further, local search operations are introduced to refine feature selection and cluster centers encoded in the chromosomes. Finally, a niching method is integrated to preserve the population diversity and prevent premature convergence. In an experimental evaluation we demonstrate the effectiveness of the proposed approach and compare it with other related approaches, using both synthetic and real data.