Fast Implementation of Double-coupled Nonnegative Canonical Polyadic Decomposition
Wang, X., Ristaniemi, T., & Cong, F. (2019). Fast Implementation of Double-coupled Nonnegative Canonical Polyadic Decomposition. In ICASSP 2019 : Proceedings of the 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (pp. 8588-8592). IEEE. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing. https://doi.org/10.1109/ICASSP.2019.8682737
Published inProceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing
© 2019 IEEE
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 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 FDCNCPD 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. ...
Parent publication ISBN978-1-4799-8131-1
ConferenceIEEE International Conference on Acoustics, Speech and Signal Processing
Is part of publicationICASSP 2019 : Proceedings of the 2019 IEEE International Conference on Acoustics, Speech and Signal Processing
Publication in research information system
MetadataShow full item record
Showing items with similar title or keywords.
Low-rank approximation based non-negative multi-way array decomposition on event-related potentials Cong, Fengyu; Zhou, Guoxu; Astikainen, Piia; Zhao, Qibin; Wu, Qiang; Nandi, Asoke; Hietanen, Jari K.; Ristaniemi, Tapani; Cichocki, Andrzej (World Scientific, 2014)Non-negative tensor factorization (NTF) has been successfully applied to analyze event-related potentials (ERPs), and shown superiority in terms of capturing multi-domain features. However, the time-frequency representation ...
Sparse nonnegative tensor decomposition using proximal algorithm and inexact block coordinate descent scheme Wang, Deqing; Chang, Zheng; Cong, Fengyu (Springer, 2021)Nonnegative tensor decomposition is a versatile tool for multiway data analysis, by which the extracted components are nonnegative and usually sparse. Nevertheless, the sparsity is only a side effect and cannot be explicitly ...
Cong, Fengyu; Lin, Qiu-Hua; Kuang, Li-Dan; Gong, Xiao-Feng; Astikainen, Piia; Ristaniemi, Tapani (Elsevier BV, 2015)Electroencephalography (EEG) is one fundamental tool for functional brain imaging. EEG signals tend to be represented by a vector or a matrix to facilitate data processing and analysis with generally understood methodologies ...
Wang, Deqing; Cong, Fengyu; Ristaniemi, Tapani (IEEE, 2019)Tensor decomposition is a powerful tool for analyzing multiway data. Nowadays, with the fast development of multisensor technology, more and more data appear in higherorder (order > 4) and nonnegative form. However, the ...
Zhu, Yongjie; Liu, Jia; Ye, Chaoxiong; Mathiak, Klaus; Astikainen, Piia; Ristaniemi, Tapani; Cong, Fengyu (Elsevier, 2020)Efficient neuronal communication between brain regions through oscillatory synchronization at certain frequencies is necessary for cognition. Such synchronized networks are transient and dynamic, established on the timescale ...