A Fast Randomized Adaptive CP Decomposition For Streaming Tensors

Le Trung Thanh; Karim Abed-Meraim; Nguyen Linh-Trung; Adel Hafiane

ICASSP 2021accepted0 citations

A Fast Randomized Adaptive CP Decomposition For Streaming Tensors

Le Trung Thanh, Karim Abed-Meraim, Nguyen Linh-Trung, Adel Hafiane

Abstract

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.

BibTeX

@inproceedings{icassp2021_afastrandomizeda,
  title = {A Fast Randomized Adaptive CP Decomposition For Streaming Tensors},
  author = {Le Trung Thanh and Karim Abed-Meraim and Nguyen Linh-Trung and Adel Hafiane},
  booktitle = {ICASSP 2021},
  year = {2021}
}