Mode Coresets for Efficient, Interpretable Tensor Decompositions: An Application to Feature Selection in fMRI Analysis
Generalizations of matrix decompositions to multidimensional arrays, called tensor decompositions, are simple yet powerful methods for analyzing datasets in the form of tensors. These decompositions model a data tensor as a sum of rank-1 tensors, whose factors provide uses for a myriad of applicatio...
Saved in:
| Main Authors: | Ben Gabrielson, Hanlu Yang, Trung Vu, Vince Calhoun, Tulay Adali |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10798430/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A Two-Level Parallel Incremental Tensor Tucker Decomposition Method with Multi-Mode Growth (TPITTD-MG)
by: Yajian Zhou, et al.
Published: (2025-04-01) -
A Simultaneous Decomposition for a Quaternion Tensor Quaternity with Applications
by: Jia-Wei Huo, et al.
Published: (2025-05-01) -
Tensor decomposition based-joint active device detection and channel estimation under frequency offset
by: QU Ruiyun, et al.
Published: (2025-06-01) -
Covid-19 pandemic data analysis using tensor methods
by: Dipak Dulal, et al.
Published: (2024-03-01) -
Functional Connectome Fingerprinting Through Tucker Tensor Decomposition
by: Vitor Carvalho, et al.
Published: (2025-04-01)