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 multi-
block tensor data, we propose a fast double-coupled nonnegative 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.