eprintid: 4453
rev_number: 8
eprint_status: archive
userid: 381
dir: disk0/00/00/44/53
datestamp: 2021-06-18 10:55:27
lastmod: 2021-06-18 10:55:27
status_changed: 2021-06-18 10:55:27
type: conference_item
metadata_visibility: show
creators_name: Le, Trung Thanh
creators_name: Karim, Abed Meraim
creators_name: Nguyen, Linh Trung
creators_name: Adel, Hafiane
creators_id: letrungthanhtbt@gmail.com
creators_id: karim.abed-meraim@univ-orleans.fr
creators_id: linhtrung@vnu.edu.vn
title: A Fast Randomized Adaptive CP Decomposition For Streaming Tensors
ispublished: pub
subjects: ElectronicsandComputerEngineering
divisions: avitech
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.
date: 2021-06-08
date_type: published
official_url: https://ieeexplore.ieee.org/abstract/document/9413554
full_text_status: none
pres_type: paper
pagerange: 2910-2914
event_title: ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
event_location: Canada
event_dates: 6-11, June 2021
event_type: conference
refereed: TRUE
citation:   Le, Trung Thanh and Karim, Abed Meraim and Nguyen, Linh Trung and Adel, Hafiane  (2021) A Fast Randomized Adaptive CP Decomposition For Streaming Tensors.  In: ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 6-11, June 2021, Canada.