TY  - CONF
ID  - SisLab4453
UR  - https://ieeexplore.ieee.org/abstract/document/9413554
A1  - Le, Trung Thanh
A1  - Karim, Abed Meraim
A1  - Nguyen, Linh Trung
A1  - Adel, Hafiane
Y1  - 2021/06/08/
N2  - In this paper, we introduce a fast adaptive algorithm for CAN- DECOMP/PARAFAC decomposition of streaming three-way tensors using randomized sketching techniques. By leveraging randomized least-squares regression and approximating matrix multiplication, we propose an efficient first-order estimator to minimize an exponentially weighted recursive least- squares cost function. Our algorithm is fast, requiring a low computational complexity and memory storage. Experiments indicate that the proposed algorithm is capable of adaptive tensor decomposition with a competitive performance evaluation on both synthetic and real data.
TI  - A Fast Randomized Adaptive CP Decomposition For Streaming Tensors
SP  - 2910
M2  - Canada
AV  - none
EP  - 2914
T2  - ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
ER  -