Release Time:2019-11-01  Hits:

Indexed by: Conference Paper

Date of Publication: 2019-01-01

Included Journals: CPCI-S、EI

Volume: 2019-May

Page Number: 8588-8592

Key Words: Tensor decomposition; coupled tensor decomposition; Hierarchical Alternating Least Squares (HALS); linked CP tensor decomposition (LCPTD)

Abstract: Real-world data exhibiting high order/dimensionality and various couplings are linked to each other since they share some common characteristics. Coupled tensor decomposition has become a popular technique for group analysis in recent years, especially for simultaneous analysis of multi-block tensor data with common information. To address the multiblock tensor data, we propose a fast double-coupled non-negative Canonical Polyadic Decomposition (FDC-NCPD) algorithm in this study, based on the linked CP tensor decomposition (LCPTD) model and fast Hierarchical Alternating Least Squares (Fast-HALS) algorithm. The proposed FDC-NCPD algorithm enables simultaneous extraction of common components, individual components and core tensors from tensor blocks. Moreover, time consumption is greatly reduced without compromising the decomposition quality when handling large-scale tensor blocks. Simulation experiments of synthetic and real-world data are conducted to demonstrate the superior performance of the proposed algorithm.