Inductive multiple clustering based on weakly-supervised salient representation learning

The increasing recognition of data diversity has highlighted multiple clustering as a valuable approach for generating diverse clustering solutions. However, conventional methods often prioritize non-redundant clusterings across disjoint subspaces, potentially overlooking key data characteristics and limiting interpretability. In this paper, we propose an Inductive Multiple Clustering (IMC) framework designed to extract distinct and interpretable representations through weakly-supervised learning from diverse clustering perspectives. Specifically, IMC decomposes data objects into group-specific salient components using reconstruction and transformation matrices with low-rank and sparse regularization. To enhance diversity among clusters, an incoherent regularization minimizes similarities between group-specific transformations in a weakly-supervised manner. Unlike previous approaches, our framework emphasizes salient representations and integrates inductive learning into multiple clustering, facilitating comprehensive interpretations of clustering results. We employ the Alternating Direction Method of Multipliers (ADMM) to optimize IMC, leveraging resulting matrices for clustering diverse datasets. Experimental results on benchmark datasets demonstrate IMC’s superiority over existing methods, providing a comprehensive explanation of multiple clustering results and successful extension to unseen data clustering.