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From One Comes Two: A Tensorized Graph Learning Framework for Clustering
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AI
DOI：10.1109/TKDE.2025.3622333.png)
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Abstract
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In this work, we focus on the task of learning the promising graph for clustering and present a novel Tensorized Graph Learning (TGL) framework, which synergizes the neighbor and self-expressiveness information. The main proposition is that the graph with neighbor information and the graph with self-expressiveness information describe the underlying clustering structure from two different perspectives and can be regarded as two views of data. To this end, our TGL converts a single-view graph learning task into a multi-view graph learning task. Specifically, it jointly learns these two graphs with a low-rank tensor constraint, which pursues the consistency in the high-order tensor space. Both the neighbor information and self-expressiveness information of data can be excavated during the graph learning process. Extensive experimental results show the promising performance of the proposed TGL in comparison to several state-of-the-art clustering algorithms.
Keywords:
Low-rank tensor constraint
graph learning
clustering
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