- Generalization of Linked Canonical Polyadic Tensor Decomposition for Group Analysis
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- 论文类型： 会议论文
- 发表时间： 2019-01-01
- 收录刊物： EI
- 卷号： 11555
- 页面范围： 180-189
- 关键字： Linked tensor decomposition; Hierarchical alternating least squares; Canonical polyadic; Simultaneous extraction
- 摘要： Real-world data are often linked with each other since they share some common characteristics. The mutual linking can be seen as a core driving force of group analysis. This study proposes a generalized linked canonical polyadic tensor decomposition (GLCPTD) model that is well suited to exploiting the linking nature in multi-block tensor analysis. To address GLCPTD model, an efficient algorithm based on hierarchical alternating least squa res (HALS) method is proposed, termed as GLCPTD-HALS algorithm. The proposed algorithm enables the simultaneous extraction of common components, individual components and core tensors from tensor blocks. Simulation experiments of synthetic EEG data analysis and image reconstruction and denoising were conducted to demonstrate the superior performance of the proposed generalized model and its realization.
