Deep clustering via dual-supervised multi-kernel mapping

doi:10.1016/j.patcog.2025.111419

Deep clustering via dual-supervised multi-kernel mapping

2025-06-01

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DOI：10.1016/j.patcog.2025.111419

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Abstract

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In this paper, we propose a novel deep clustering framework via dual-supervised multi-kernel mapping, namely DCDMK, to improve clustering performance by learning linearly structural separable data representations. In the DCDMK framework, we introduce a kernel-aid encoder comprising two key components: a semantic representation learner, which captures the essential semantic information for clustering, and a multi-kernel representation learner, which dynamically selects the optimal combination of kernel functions through dual- supervised multi-kernel mapping to learn structurally separable kernel representations. The dual self-supervised mechanism is devised to jointly optimize both kernel representation learning and structural partitioning. Based on this framework, we introduce different fusion strategies to learn the multi-kernel representation of data samples for the clustering task. We derive two variants, namely DCDMK-WL (with layer-level kernel representation learning) and DCDMK-OL (without layer-level kernel representation learning). Extensive experiments on six real-world datasets demonstrate the effectiveness of our DCDMK framework.

Keywords:

Deep clustering

Multiple kernel

Kernel representation

Semantic representation

Deep clustering via dual-supervised multi-kernel mapping

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