Results 31 to 40 of about 1,952,092 (277)
Spectral Norm and Nuclear Norm of a Third Order Tensor
CoRR, 2019 The spectral norm and the nuclear norm of a third order tensor play an important role in the tensor completion and recovery problem. We show that the spectral norm of a third order tensor is equal to the square root of the spectral norm of three positive semi-definite biquadratic tensors, and the square roots of the nuclear norms of those three ...
Liqun Qi 0001, Shenglong Hu
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Bi-nuclear tensor Schatten-p norm minimization for multi-view subspace clustering [PDF]
, 2023 Multi-view subspace clustering aims to integrate the complementary information contained in different views to facilitate data representation. Currently, low-rank representation (LRR) serves as a benchmark method.
Yigang Cen +9 more
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IEEE Access, 2018 The robust and efficient detection of infrared small target is a key technique for infrared search and track systems. Several robust principal component analysis (RPCA)-based methods have been developed recently, which have achieved the state-of-the-art ...
Yang Sun +4 more
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On the Nuclear Norm and the Singular Value Decomposition of Tensors [PDF]
Foundations of Computational Mathematics, 2015 Finding the rank of a tensor is a problem that has many applications. Unfortunately it is often very difficult to determine the rank of a given tensor. Inspired by the heuristics of convex relaxation, we consider the nuclear norm instead of the rank of a tensor. We determine the nuclear norm of various tensors of interest.
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A Hybrid Norm for Guaranteed Tensor Recovery
Frontiers in Physics, 2022 Benefiting from the superiority of tensor Singular Value Decomposition (t-SVD) in excavating low-rankness in the spectral domain over other tensor decompositions (like Tucker decomposition), t-SVD-based tensor learning has shown promising performance and
Yihao Luo +5 more
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Alternating Direction Method of Multipliers for Generalized Low-Rank Tensor Recovery
Algorithms, 2016 Low-Rank Tensor Recovery (LRTR), the higher order generalization of Low-Rank Matrix Recovery (LRMR), is especially suitable for analyzing multi-linear data with gross corruptions, outliers and missing values, and it attracts broad attention in the fields
Jiarong Shi +3 more
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Nuclear norm of higher-order tensors [PDF]
, 2016 We establish several mathematical and computational properties of the nuclear norm for higher-order tensors. We show that like tensor rank, tensor nuclear norm is dependent on the choice of base field --- the value of the nuclear norm of a real 3-tensor ...
Shmuel Friedland (7936745) +1 more
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Cirrus Detection Based on Tensor Multi-Mode Expansion Sum Nuclear Norm in Infrared Imagery
IEEE Access, 2020 Infrared small target detection systems are an important part of space infrared imaging satellites. However, small infrared target detection is often affected by cirrus false alarm sources with similar grayscales.
Chunping Yang +3 more
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A QoS Prediction Approach Based on Truncated Nuclear Norm Low-Rank Tensor Completion
Sensors, 2022 With the rise of mobile edge computing (MEC), mobile services with the same or similar functions are gradually increasing. Usually, Quality of Service (QoS) has become an indicator to measure high-quality services.
Hong Xia +5 more
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A Non-Local Tensor Completion Algorithm Based on Weighted Tensor Nuclear Norm
, 2022 In this paper, we proposed an image inpainting algorithm, including an interpolation step and a non-local tensor completion step based on a weighted tensor nuclear norm.
Jingjing Zheng +4 more
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