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Fast Implementation of Double-coupled Nonnegative Canonical Polyadic Decomposition

Citation Author(s):
Tapani Ristaniemi, Fengyu Cong
Submitted by:
Xiulin Wang
Last updated:
8 May 2019 - 6:25am
Document Type:
Poster
Document Year:
2019
Event:
Presenters:
Xiulin Wang
Paper Code:
#2370
 

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.

https://ieeexplore.ieee.org/document/8682737

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