Results 221 to 230 of about 17,487 (260)
Connections Between Nuclear-Norm and Frobenius-Norm-Based Representations [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2018 A lot of works have shown that frobenius-norm based representation (FNR) is competitive to sparse representation and nuclear-norm based representation (NNR) in numerous tasks such as subspace clustering. Despite the success of FNR in experimental studies, less theoretical analysis is provided to understand its working mechanism.
Xi Peng, Canyi Lu, Huajin Tang
exaly +4 more sources
Bounds on the Spectral Norm and the Nuclear Norm of a Tensor Based on Tensor Partitions [PDF]
SIAM Journal on Matrix Analysis and Applications, 2016 Summary: It is known that computing the spectral norm and the nuclear norm of a tensor is NP-hard in general. In this paper, we provide neat bounds for the spectral norm and the nuclear norm of a tensor based on tensor partitions. The spectral norm (respectively, the nuclear norm) can be lower and upper bounded by manipulating the spectral norms ...
Zhening Li
exaly +4 more sources
Some of the next articles are maybe not open access.
Related searches:
Related searches:
2013 2nd IAPR Asian Conference on Pattern Recognition, 2013
This paper presents a novel method, namely nuclear norm based 2DPCA (N-2DPCA), for image feature extraction. Unlike the conventional 2DPCA, N-2DPCA uses a nuclear norm based reconstruction error criterion. The criterion is minimized by converting the nuclear norm based optimization problem into a series of F-norm based optimization problems. N-2DPCA is
Fanlong Zhang +2 more
openaire +1 more source
This paper presents a novel method, namely nuclear norm based 2DPCA (N-2DPCA), for image feature extraction. Unlike the conventional 2DPCA, N-2DPCA uses a nuclear norm based reconstruction error criterion. The criterion is minimized by converting the nuclear norm based optimization problem into a series of F-norm based optimization problems. N-2DPCA is
Fanlong Zhang +2 more
openaire +1 more source
Nuclear Norm Regularized Sparse Coding
2014 22nd International Conference on Pattern Recognition, 2014Partially occluded or illuminated faces pose a significant obstacle for robust, real-world face recognition. The problem of how to characterize the error caused by occlusion or illumination is still a challenging task. There must exist some close relationship between the error metric and error distribution. However, some metric (e.g.
Lei Luo 0001 +3 more
openaire +1 more source
Nuclear norm-regularized SENSE reconstruction
Magnetic Resonance Imaging, 2012SENSitivity Encoding (SENSE) is a mathematically optimal parallel magnetic resonance (MRI) imaging technique when the coil sensitivities are known. In recent times, compressed sensing (CS)-based techniques are incorporated within the SENSE reconstruction framework to recover the underlying MR image.
Angshul, Majumdar, Rabab K, Ward
openaire +2 more sources
Dynamic MRI Reconstruction Combining Tensor Nuclear Norm and Casorati Matrix Nuclear Norm
ISMRM Annual Meeting, 2023Low-rank tensor models have been applied in accelerating dynamic magnetic resonance imaging (dMRI). Recently, a new tensor nuclear norm based on t-SVD has been proposed and applied to tensor completion. Inspired by the different properties of the tensor nuclear norm (TNN) and the Casorati matrix nuclear norm (MNN), we introduce a novel dMRI ...
Zhang, Yinghao, Hu, Yue, Lu, Xin
openaire +2 more sources
Nuclear Norms and German Nuclear Interests
Comparative Strategy, 2001A broad norm against nuclear weapons has not established itself in Germany. A brief overview of Germany's current "non-debate" regarding nuclear weapons shows that discussion concentrates on related issues like proliferation, missile defense, and arms control, and not on NATO strategy per se.
openaire +1 more source
On the nuclear norm approach to interference alignment
2012 Conference Record of the Forty Sixth Asilomar Conference on Signals, Systems and Computers (ASILOMAR), 2012This paper considers a K-user multiple-input multiple-output (MIMO) interference channel in which un-coordinated interference appears. Due to the uncoordinated interference, perfect interference alignment (IA) may be not attained. In order to maximize the achievable degrees-of-freedom (DoF) per user, the interference alignment is formulated as a rank ...
Huiqin Du, Tharm Ratnarajah
openaire +1 more source
Nuclear Norm Based Bidirectional 2DPCA
2014This paper develops a new image feature extraction and recognition method coined bidirectional compressed nuclear-norm based 2DPCA (BN2DPCA). BN2DPCA presents a sequentially optimal image compression mechanism, making the information of the image compact into its up-left corner. BN2DPCA is tested using the Extended Yale B and the CMU PIE face databases.
Yu Ding, Caikou Chen, Ya Gu, Yu Wang
openaire +1 more source
WIREs Computational Statistics
ABSTRACTNuclear norm, also known as trace norm, has been widely used in statistical machine learning. Nuclear norm regularization has emerged as an important tool for addressing various statistical problems involving the estimation of low‐rank matrices, particularly in tasks such as matrix completion and reduced rank regression. This review delves into
Dengdeng Yu, Dehan Kong
openaire +1 more source
ABSTRACTNuclear norm, also known as trace norm, has been widely used in statistical machine learning. Nuclear norm regularization has emerged as an important tool for addressing various statistical problems involving the estimation of low‐rank matrices, particularly in tasks such as matrix completion and reduced rank regression. This review delves into
Dengdeng Yu, Dehan Kong
openaire +1 more source