Robust low-rank tensor modelling using Tucker and CP decomposition

Conference paper


Xue, N., Papamakarios, G., Bahri, M., Panagakis, Y. and Zafeiriou, S. 2017. Robust low-rank tensor modelling using Tucker and CP decomposition. 2017 25th European Signal Processing Conference (EUSIPCO). Kos, Greece 28 Aug - 02 Sep 2017 Institute of Electrical and Electronics Engineers (IEEE). pp. 1185-1189 https://doi.org/10.23919/EUSIPCO.2017.8081395
TypeConference paper
TitleRobust low-rank tensor modelling using Tucker and CP decomposition
AuthorsXue, N., Papamakarios, G., Bahri, M., Panagakis, Y. and Zafeiriou, S.
Abstract

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.

LanguageEnglish
Conference2017 25th European Signal Processing Conference (EUSIPCO)
Page range1185-1189
ISSN2076-1465
ISBN
Hardcover9780992862671
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Publication dates
Print26 Oct 2017
Publication process dates
Deposited06 Mar 2018
Accepted25 May 2017
Output statusPublished
Digital Object Identifier (DOI)https://doi.org/10.23919/EUSIPCO.2017.8081395
Book title2017 25th European Signal Processing Conference (EUSIPCO)
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