Generalization of Linked Canonical Polyadic Tensor Decomposition for Group Analysis
Wang, X., Zhang, C., Ristaniemi, T., & Cong, F. (2019). Generalization of Linked Canonical Polyadic Tensor Decomposition for Group Analysis. In H. Lu, H. Tang, & Z. Wang (Eds.), ISNN 2019 : Advances in Neural Networks : 16th International Symposium on Neural Networks, Proceedings, Part II (pp. 180-189). Springer International Publishing. Lecture Notes in Computer Science, 11555. https://doi.org/10.1007/978-3-030-22808-8_19
Published inLecture Notes in Computer Science
© Springer Nature Switzerland AG 2019.
Real-world data are often linked with each other since they share some common characteristics. The mutual linking can be seen as a core driving force of group analysis. This study proposes a generalized linked canonical polyadic tensor decomposition (GLCPTD) model that is well suited to exploiting the linking nature in multi-block tensor analysis. To address GLCPTD model, an efficient algorithm based on hierarchical alternating least squa res (HALS) method is proposed, termed as GLCPTD-HALS algorithm. The proposed algorithm enables the simultaneous extraction of common components, individual components and core tensors from tensor blocks. Simulation experiments of synthetic EEG data analysis and image reconstruction and denoising were conducted to demonstrate the superior performance of the proposed generalized model and its realization.
PublisherSpringer International Publishing
Parent publication ISBN978-3-030-22807-1
ConferenceInternational Symposium on Neural Networks
Is part of publicationISNN 2019 : Advances in Neural Networks : 16th International Symposium on Neural Networks, Proceedings, Part II
Publication in research information system
MetadataShow full item record
Showing items with similar title or keywords.
Multi-domain Features of the Non-phase-locked Component of Interest Extracted from ERP Data by Tensor Decomposition Zhang, Guanghui; Zhang, Chi; Cao, Shuo; Xia, Xue; Tan, Xin; Si, Lichengxi; Wang, Chenxin; Wang, Xiaochun; Zhou, Chenglin; Ristaniemi, Tapani; Cong, Fengyu (Springer, 2020)The waveform in the time domain, spectrum in the frequency domain, and topography in the space domain of component(s) of interest are the fundamental indices in neuroscience research. Despite the application of time–frequency ...
Wang, Deqing; Wang, Xiaoyu; Zhu, Yongjie; Toiviainen, Petri; Huotilainen, Minna; Ristaniemi, Tapani; Cong, Fengyu (Springer International Publishing, 2018)Tensor decomposition has been widely employed for EEG signal processing in recent years. Constrained and regularized tensor decomposition often attains more meaningful and interpretable results. In this study, we applied ...
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 ...
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 ...
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 ...