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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence

Robust Tensor Decomposition via Orientation Invariant Tubal Nuclear Norms

February 1, 2023

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Authors

Andong Wang

NJUST


Chao Li

RIKEN


Zhong Jin

NJUST


Qibin Zhao

RIKEN


DOI:

10.1609/aaai.v34i04.6074


Abstract:

Low-rank tensor recovery has been widely applied to computer vision and machine learning. Recently, tubal nuclear norm (TNN) based optimization is proposed with superior performance as compared to other tensor nuclear norms. However, one major limitation is its orientation sensitivity due to low-rankness strictly defined along tubal orientation and it cannot simultaneously model spectral low-rankness in multiple orientations. To this end, we introduce two new tensor norms called OITNN-O and OITNN-L to exploit multi-orientational spectral low-rankness for an arbitrary K-way (K ≥ 3) tensors. We further formulate two robust tensor decomposition models via the proposed norms and develop two algorithms as the solutions. Theoretically, we establish non-asymptotic error bounds which can predict the scaling behavior of the estimation error. Experiments on real-world datasets demonstrate the superiority and effectiveness of the proposed norms.

Topics: AAAI

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HOW TO CITE:

Andong Wang||Chao Li||Zhong Jin||Qibin Zhao Robust Tensor Decomposition via Orientation Invariant Tubal Nuclear Norms Proceedings of the AAAI Conference on Artificial Intelligence (2020) 6102-6109.

Andong Wang||Chao Li||Zhong Jin||Qibin Zhao Robust Tensor Decomposition via Orientation Invariant Tubal Nuclear Norms AAAI 2020, 6102-6109.

Andong Wang||Chao Li||Zhong Jin||Qibin Zhao (2020). Robust Tensor Decomposition via Orientation Invariant Tubal Nuclear Norms. Proceedings of the AAAI Conference on Artificial Intelligence, 6102-6109.

Andong Wang||Chao Li||Zhong Jin||Qibin Zhao. Robust Tensor Decomposition via Orientation Invariant Tubal Nuclear Norms. Proceedings of the AAAI Conference on Artificial Intelligence 2020 p.6102-6109.

Andong Wang||Chao Li||Zhong Jin||Qibin Zhao. 2020. Robust Tensor Decomposition via Orientation Invariant Tubal Nuclear Norms. "Proceedings of the AAAI Conference on Artificial Intelligence". 6102-6109.

Andong Wang||Chao Li||Zhong Jin||Qibin Zhao. (2020) "Robust Tensor Decomposition via Orientation Invariant Tubal Nuclear Norms", Proceedings of the AAAI Conference on Artificial Intelligence, p.6102-6109

Andong Wang||Chao Li||Zhong Jin||Qibin Zhao, "Robust Tensor Decomposition via Orientation Invariant Tubal Nuclear Norms", AAAI, p.6102-6109, 2020.

Andong Wang||Chao Li||Zhong Jin||Qibin Zhao. "Robust Tensor Decomposition via Orientation Invariant Tubal Nuclear Norms". Proceedings of the AAAI Conference on Artificial Intelligence, 2020, p.6102-6109.

Andong Wang||Chao Li||Zhong Jin||Qibin Zhao. "Robust Tensor Decomposition via Orientation Invariant Tubal Nuclear Norms". Proceedings of the AAAI Conference on Artificial Intelligence, (2020): 6102-6109.

Andong Wang||Chao Li||Zhong Jin||Qibin Zhao. Robust Tensor Decomposition via Orientation Invariant Tubal Nuclear Norms. AAAI[Internet]. 2020[cited 2023]; 6102-6109.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

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