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Low-Rank Tensor Regularized Views Recovery for Incomplete Multiview Clustering

Zhang, Chao ; Li, Huaxiong ; Chen, Caihua ; Jia, Xiuyi ; Chen, Chunlin

IEEE transaction on neural networks and learning systems, 2022-12, Vol.PP, p.1-13

United States: IEEE

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  • Título:
    Low-Rank Tensor Regularized Views Recovery for Incomplete Multiview Clustering
  • Autor: Zhang, Chao ; Li, Huaxiong ; Chen, Caihua ; Jia, Xiuyi ; Chen, Chunlin
  • Assuntos: Incomplete multiview clustering (IMVC) ; latent subspace ; missing views recovery ; tensor nuclear norm
  • É parte de: IEEE transaction on neural networks and learning systems, 2022-12, Vol.PP, p.1-13
  • Notas: ObjectType-Article-1
    SourceType-Scholarly Journals-1
    ObjectType-Feature-2
  • Descrição: In real applications, it is often that the collected multiview data contain missing views. Most existing incomplete multiview clustering (IMVC) methods cannot fully utilize the underlying information of missing data or sufficiently explore the consistent and complementary characteristics. In this article, we propose a novel Low-rAnk Tensor regularized viEws Recovery (LATER) method for IMVC, which jointly reconstructs and utilizes the missing views and learns multilevel graphs for comprehensive similarity discovery in a unified model. The missing views are recovered from a common latent representation, and the recovered views conversely improve the learning of shared patterns. Based on the shared subspace representations and recovered complete multiview data, the multilevel graphs are learned by self-representation to fully exploit the consistent and complementary information among views. Besides, a tensor nuclear norm regularizer is introduced to pursue the global low-rank property and explore the interview correlations. An alternating direction minimization algorithm is presented to optimize the proposed model. Moreover, a new initialization method is proposed to promote the effectiveness of our method for latent representation learning and missing data recovery. Extensive experiments demonstrate that our method outperforms the state-of-the-art approaches.
  • Editor: United States: IEEE
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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