Robust low-rank tensor modelling using Tucker and CP decomposition

Xue, Niannan, Papamakarios, George, Bahri, Mehdi, Panagakis, Yannis ORCID logoORCID: and Zafeiriou, Stefanos (2017) Robust low-rank tensor modelling using Tucker and CP decomposition. 2017 25th European Signal Processing Conference (EUSIPCO). In: 2017 25th European Signal Processing Conference (EUSIPCO), 28 Aug - 02 Sept 2017, Kos, Greece. ISBN 9780992862671. ISSN 2076-1465 [Conference or Workshop Item] (doi:10.23919/EUSIPCO.2017.8081395)


A framework for reliable seperation of a low-rank subspace from grossly corrupted multi-dimensional signals is pivotal in modern signal processing applications. Current methods fall short of this separation either due to the radical simplification or the drastic transformation of data. This has motivated us to propose two new robust low-rank tensor models: Tensor Orthonormal Robust PCA (TORCPA) and Tensor Robust CP Decomposition (TRCPD). They seek Tucker and CP decomposition of a tensor respectively with lp norm regularisation. We compare our methods with state-of-the-art low-rank models on both synthetic and real-world data. Experimental results indicate that the proposed methods are faster and more accurate than the methods they compared to.

Item Type: Conference or Workshop Item (Paper)
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 23784
Useful Links:
Depositing User: Yannis Panagakis
Date Deposited: 06 Mar 2018 19:19
Last Modified: 08 Jan 2019 14:59

Actions (login required)

View Item View Item


Activity Overview
6 month trend
6 month trend

Additional statistics are available via IRStats2.