会议论文

Zhang, JM

英文

2023 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, IJCNN

2161-4393

2023

International Joint Conference on Neural Networks (IJCNN)

IEEE International Joint Conference on Neural Networks (IJCNN)

JUN 18-23, 2023

Broadbeach, AUSTRALIA

[Kuang, Li-Dan;Zhang, Hao-Peng;Zhang, Jianming] Changsha Univ Sci & Technol, Sch Comp & Commun Engn, Changsha 410114, Peoples R China.

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

978-1-6654-8867-9

10.1109/IJCNN54540.2023.10191856

National Natural Science Foundation of China [61901061, 61972056]; Scientific Research Fund of Hunan Provincial Education Department [22B0341]; Postgraduate Research Innovation Project of Changsha University of Science and Technology [CX2021SS75]

本校为第一且通讯机构

The rank-(L,L,1,1) block term decomposition (BTD) with spatial orthonormality (BTD-O) applied to 4-way multi-subject fMRI data achieves good performance due to preserving higher spatial structure and reducing crosstalk between components. However, the high rank L value (e.g., 35) of BTD-O for fMRI data leads to high computation complexity. Moreover, multi-subject fMRI data contains high noise nature. Although an accelerated BTD-O (accBTD-O) was proposed, it showed similar performance to BTD-O. Inspired by the compression, smoothing, and spatial structure invariance features of the pooling sche...