Double Coupled Canonical Polyadic Decomposition for Joint Blind Source Separation
Double Coupled Canonical Polyadic Decomposition for Joint Blind Source Separation
复制标题
联合盲源分离的双耦合正则多元分解
DOI：
10.1109/tsp.2018.2830317
复制
发表时间：
2018-07-01
影响因子：
5.4
通讯作者：
De Lathauwer, Lieven
中科院分区：
文献类型：
--
作者：
Gong, Xiao-Feng;Lin, Qiu-Hua;De Lathauwer, Lieven
Joint blind source separation (J-BSS) is an emerging data-driven technique for multi-set data-fusion. In this paper, J-BSS is addressed from a tensorial perspective. We show how, by using second-order multi-set statistics in J-BSS, a specific double coupled canonical polyadic decomposition (DC-CPD) problem can be formulated. We propose an algebraic DC-CPD algorithm based on a coupled rank-1 detection mapping. This algorithm converts a possibly underdetermined DC-CPD to a set of overdetermined CPDs. The latter can be solved algebraically via a generalized eigenvalue decomposition based scheme. Therefore, this algorithm is deterministic and returns the exact solution in the noiseless case. In the noisy case, it can be used to effectively initialize optimization based DC-CPD algorithms. In addition, we obtain the deterministic and generic uniqueness conditions for DC-CPD, which are shown to be more relaxed than their CPD counterpart. We also introduce optimization based DC-CPD methods, including alternating least squares, and structured data fusion based methods. Experiment results are given to illustrate the superiority of DC-CPD over standard CPD based BSS methods and several existing J-BSS methods, with regards to uniqueness and accuracy.
