6533b7d4fe1ef96bd12628d8

RESEARCH PRODUCT

Generalization of Linked Canonical Polyadic Tensor Decomposition for Group Analysis

Tapani RistaniemiXiulin WangFengyu CongChi Zhang

subject

canonical polyadicComputer scienceGeneralizationNoise reductionlinked tensor decomposition020206 networking & telecommunications02 engineering and technologyIterative reconstructionhierarchical alternating least squares03 medical and health sciencessimultaneous extraction0302 clinical medicineGroup analysisCore (graph theory)0202 electrical engineering electronic engineering information engineeringTensor decompositionTensorAlgorithmRealization (systems)030217 neurology & neurosurgery

description

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. peerReviewed

yearjournalcountryeditionlanguage
2019-01-01
https://doi.org/10.1007/978-3-030-22808-8_19