%A Trung Thanh Le
%A Abed Meraim Karim
%A Linh Trung Nguyen
%A Hafiane Adel
%T A Fast Randomized Adaptive CP Decomposition For Streaming Tensors
%X 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.
%C Canada
%D 2021
%P 2910-2914
%L SisLab4453