2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

Technical Program

Paper Detail

Paper IDSAM-13.2
Paper Title Canonical Polyadic Tensor Decomposition with Low-Rank Factor Matrices
Authors Anh-Huy Phan, Skolkovo Institute of Science and Technology, Russia; Petr Tichavsky, The Czech Academy of Sciences, Institute of Information Theory and Automation, Czechia; Konstantin Sobolev, Konstantin Sozykin, Dmitry Ermilov, Andrzej Cichocki, Skolkovo Institute of Science and Technology, Russia
SessionSAM-13: Multi-Channel Data Fusion and Processing
LocationGather.Town
Session Time:Friday, 11 June, 14:00 - 14:45
Presentation Time:Friday, 11 June, 14:00 - 14:45
Presentation Poster
Topic Sensor Array and Multichannel Signal Processing: [SAM-TNSR] Tensor-based signal processing for multi-sensor systems
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Virtual Presentation  Click here to watch in the Virtual Conference
Abstract This paper proposes a constrained canonical polyadic (CP) tensor decomposition method with low-rank factor matrices. In this way, we allow the CP decomposition with high rank while keeping the number of the model parameters small. First, we propose an algorithm to decompose the tensors into factor matrices of given ranks. Second, we propose an algorithm which can determine the ranks of the factor matrices automatically, such that the fitting error is bounded by a user-selected constant. The algorithms are verified on the decomposition of a tensor of the MNIST hand-written image dataset.