OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!
If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.
Requested Article:
Constrained CPD of Complex-Valued Multi-Subject fMRI Data via Alternating Rank-R and Rank-1 Least Squares
Li‐Dan Kuang, Qiu‐Hua Lin, Xiao‐Feng Gong, et al.
IEEE Transactions on Neural Systems and Rehabilitation Engineering (2022) Vol. 30, pp. 2630-2640
Open Access | Times Cited: 3
Li‐Dan Kuang, Qiu‐Hua Lin, Xiao‐Feng Gong, et al.
IEEE Transactions on Neural Systems and Rehabilitation Engineering (2022) Vol. 30, pp. 2630-2640
Open Access | Times Cited: 3
Showing 3 citing articles:
Constrained coupled CPD of complex-valued multi-slice multi-subject fMRI data
Li‐Dan Kuang, Hao Zhu, Long Lei, et al.
Signal Processing (2025) Vol. 234, pp. 110004-110004
Closed Access
Li‐Dan Kuang, Hao Zhu, Long Lei, et al.
Signal Processing (2025) Vol. 234, pp. 110004-110004
Closed Access
A core tensor sparsity enhancement method for solving Tucker-2 model of multi-subject fMRI data
Han Yue, Qiu‐Hua Lin, Li‐Dan Kuang, et al.
Biomedical Signal Processing and Control (2024) Vol. 95, pp. 106471-106471
Closed Access | Times Cited: 1
Han Yue, Qiu‐Hua Lin, Li‐Dan Kuang, et al.
Biomedical Signal Processing and Control (2024) Vol. 95, pp. 106471-106471
Closed Access | Times Cited: 1
Incorporating Spatial Sparsity Constraint into Complex IVA of Multi-subject Complex-Valued fMRI Data
Chaoying Zhang, Weixing Li, Li‐Dan Kuang, et al.
2022 International Joint Conference on Neural Networks (IJCNN) (2023), pp. 1-6
Closed Access
Chaoying Zhang, Weixing Li, Li‐Dan Kuang, et al.
2022 International Joint Conference on Neural Networks (IJCNN) (2023), pp. 1-6
Closed Access
