Fast Adaptive Cross Tubal Tensor Approximation
- Resource Type
- Conference
- Authors
- Ahmadi-Asl, Salman; Phan, Anh-Huy; Cichocki, Andrzej; Jha, Ashish; Sozykina, Anastasia; Wang, Jun; Oseledets, Ivan
- Source
- 2023 IEEE Statistical Signal Processing Workshop (SSP) Statistical Signal Processing Workshop (SSP), 2023 IEEE. :552-556 Jul, 2023
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Computing and Processing
Engineering Profession
Power, Energy and Industry Applications
Signal Processing and Analysis
Deep learning
Adaptation models
Tensors
Conferences
Computational modeling
Signal processing algorithms
Signal processing
tensors
tensor SVD
cross approximation
- Language
- ISSN
- 2693-3551
This paper deals with proposing a new efficient adaptive algorithm for the computation of tensor SVD (t-SVD). The proposed algorithm can estimate the tubal-rank of a given third-order tensor and the corresponding low tubal-rank approximation given an approximation tolerance. The main advantage of the proposed algorithm is using only a part of lateral and a horizontal slices at each iteration in its computations. So, it is applicable for decomposing large-scale data tensors. Simulations on synthetics and real-world datasets are provided and in some cases, we achieve more than one order of magnitude acceleration compared with the classical truncated t-SVD. It is shown that the proposed approach can potentially be used in deep learning and internet of things applications.