Aiming at recovering an unknown tensor(i.e.,multi-way array)corrupted by both sparse outliers and dense noises,robust tensor decomposition(RTD)serves as a powerful pre-processing tool for subsequent tasks like classif...Aiming at recovering an unknown tensor(i.e.,multi-way array)corrupted by both sparse outliers and dense noises,robust tensor decomposition(RTD)serves as a powerful pre-processing tool for subsequent tasks like classification and target detection in many computer vision and machine learning applications.Recently,tubal nuclear norm(TNN)based optimization is proposed with superior performance as compared with other tensorial nuclear norms for tensor recovery.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 RTD 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.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.61872188,62103110,62073087,62071132,61903095,U191140003,and 61973090)the China Postdoctoral Science Foundation(Grant No.2020M672536)+1 种基金the Natural Science Foundation of Guangdong Province(Grant Nos.2020A1515010671,2019B010154002,and 2019B010118001)the Guangdong Provincial Key Laboratory of Electronic Information Products Reliability Technology(Grant No.2017B030314151)。
文摘Aiming at recovering an unknown tensor(i.e.,multi-way array)corrupted by both sparse outliers and dense noises,robust tensor decomposition(RTD)serves as a powerful pre-processing tool for subsequent tasks like classification and target detection in many computer vision and machine learning applications.Recently,tubal nuclear norm(TNN)based optimization is proposed with superior performance as compared with other tensorial nuclear norms for tensor recovery.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 RTD 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.
