A Self-Organizing Tensor Architecture for Multi-View Clustering
This addresses the challenge of effectively clustering unlabeled data with multiple complementary views, which is incremental as it builds on existing multi-view clustering approaches.
The paper tackles the problem of multi-view clustering by proposing a tensor-based method to capture higher-order interactions between features across views, demonstrating clear outperformance over state-of-the-art methods in experiments on real-world datasets.
In many real-world applications, data are often unlabeled and comprised of different representations/views which often provide information complementary to each other. Although several multi-view clustering methods have been proposed, most of them routinely assume one weight for one view of features, and thus inter-view correlations are only considered at the view-level. These approaches, however, fail to explore the explicit correlations between features across multiple views. In this paper, we introduce a tensor-based approach to incorporate the higher-order interactions among multiple views as a tensor structure. Specifically, we propose a multi-linear multi-view clustering (MMC) method that can efficiently explore the full-order structural information among all views and reveal the underlying subspace structure embedded within the tensor. Extensive experiments on real-world datasets demonstrate that our proposed MMC algorithm clearly outperforms other related state-of-the-art methods.