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Joint Projection Learning and Tensor Decomposition-Based Incomplete Multiview Clustering

Lv, Wei ; Zhang, Chao ; Li, Huaxiong ; Jia, Xiuyi ; Chen, Chunlin

IEEE transaction on neural networks and learning systems, 2023-08, Vol.PP, p.1-12

United States: IEEE

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  • Título:
    Joint Projection Learning and Tensor Decomposition-Based Incomplete Multiview Clustering
  • Autor: Lv, Wei ; Zhang, Chao ; Li, Huaxiong ; Jia, Xiuyi ; Chen, Chunlin
  • Assuntos: Correlation ; Graph learning ; incomplete multiview clustering (IMVC) ; Indexes ; Kernel ; Learning systems ; Matrix decomposition ; Optimization ; projection learning ; tensor decomposition ; Tensors
  • É parte de: IEEE transaction on neural networks and learning systems, 2023-08, Vol.PP, p.1-12
  • Notas: ObjectType-Article-1
    SourceType-Scholarly Journals-1
    ObjectType-Feature-2
    content type line 23
  • Descrição: Incomplete multiview clustering (IMVC) has received increasing attention since it is often that some views of samples are incomplete in reality. Most existing methods learn similarity subgraphs from original incomplete multiview data and seek complete graphs by exploring the incomplete subgraphs of each view for spectral clustering. However, the graphs constructed on the original high-dimensional data may be suboptimal due to feature redundancy and noise. Besides, previous methods generally ignored the graph noise caused by the interclass and intraclass structure variation during the transformation of incomplete graphs and complete graphs. To address these problems, we propose a novel joint projection learning and tensor decomposition (JPLTD)-based method for IMVC. Specifically, to alleviate the influence of redundant features and noise in high-dimensional data, JPLTD introduces an orthogonal projection matrix to project the high-dimensional features into a lower-dimensional space for compact feature learning. Meanwhile, based on the lower-dimensional space, the similarity graphs corresponding to instances of different views are learned, and JPLTD stacks these graphs into a third-order low-rank tensor to explore the high-order correlations across different views. We further consider the graph noise of projected data caused by missing samples and use a tensor-decomposition-based graph filter for robust clustering. JPLTD decomposes the original tensor into an intrinsic tensor and a sparse tensor. The intrinsic tensor models the true data similarities. An effective optimization algorithm is adopted to solve the JPLTD model. Comprehensive experiments on several benchmark datasets demonstrate that JPLTD outperforms the state-of-the-art methods. The code of JPLTD is available at https://github.com/weilvNJU/JPLTD.
  • Editor: United States: IEEE
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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