ArticleJournal of multivariate analysis2026
Hierarchical structure-guided high-dimensional multi-view clustering.
Article in Journal of multivariate analysis, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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4 authors.
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Abstract
Multi-view data clustering is pivotal for comprehending the heterogeneous structure of data by integrating information from diverse aspects. Nevertheless, practical challenges arise due to the differences in the granularity from different views, resulting in a hierarchical clustering structure within these distinct data types. In this work, we consider such structure information and propose a novel high-dimensional multi-view clustering approach with a hierarchical structure across views. The proposed non-convex problem is effectively tackled using the Alternating Direction Method of Multipliers algorithm, and we establish the statistical properties of the estimator. Simulation results demonstrate the effectiveness and superiority of our proposed method. In the analysis of the histopathological imaging data and gene expression data related to lung adenocarcinoma, our method unveils a hierarchical clustering structure that significantly diverges from alternative approaches.
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