2018/10/18 by Lifang He, Chun-Ta Lu, Chun-ta Lu +12
Computer Science · Mathematics · #15A69 #53A45 #62H30 #FOS: Computer and information sciences #Face and Expression Recognition #I.5.3 #Image Retrieval and Classification Techniques #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Video Surveillance and Tracking Methods #acm:15A69 #acm:53A45 #acm:62H30 #cs.LG #msc:15A69 #msc:53A45 #msc:62H30 #stat.ML
paper · pdf · doi:10.48550/arxiv.1810.07874
2018 IEEE International Conference on Data Mining (ICDM)
arxiv created 2018/10/18 · openalex publication_date 2018/10/18 · arxiv updated 2018/10/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.