Results 11 to 20 of about 1,958,095 (294)

Interference alignment using reweighted nuclear norm minimization [PDF]

2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013
This paper proposes an algorithm to compute the transmit beamformers for linear interference alignment for the MIMO interference channel and the MIMO interfering multiple-access/broadcast channel without symbol extensions. We first formulate the interference alignment problem as a rank minimization problem with linear constraints, then approximate the ...
Gokul Sridharan, Wei Yu 0001
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Quaternion Nuclear Norm Minus Frobenius Norm Minimization for color image reconstruction [PDF]

Pattern Recognition
This paper was accepted by Pattern Recognition on September 5 ...
Yu Guo 0008, Guoqing Chen, Tieyong Zeng, Qiyu Jin, Michael Kwok-Po Ng   +4 more
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Distributed nuclear norm minimization for matrix completion

2012 IEEE 13th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2012
The ability to recover a low-rank matrix from a subset of its entries is the leitmotif of recent advances for localization of wireless sensors, unveiling traffic anomalies in backbone networks, and preference modeling for recommender systems. This paper develops a distributed algorithm for low-rank matrix completion over networks.
Morteza Mardani, Gonzalo Mateos, Georgios B. Giannakis   +2 more
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On the Optimal Solution of Weighted Nuclear Norm Minimization

CoRR, 2014
In recent years, the nuclear norm minimization (NNM) problem has been attracting much attention in computer vision and machine learning. The NNM problem is capitalized on its convexity and it can be solved efficiently. The standard nuclear norm regularizes all singular values equally, which is however not flexible enough to fit real scenarios. Weighted
Qi Xie 0002, Deyu Meng, Shuhang Gu, Lei Zhang 0006, Wangmeng Zuo, Xiangchu Feng, Zongben Xu   +6 more
openaire   +3 more sources

Weighted Tensor Nuclear Norm Minimization for Color Image Restoration [PDF]

IEEE Access, 2019
Non-local self-similarity (NLSS) is widely used as prior information in an image restoration method. In particular, a low-rankness-based prior has a significant effect on performance.
Kaito Hosono, Shunsuke Ono, Takamichi Miyata   +2 more
doaj   +3 more sources

Joint Frobenius norm and reweighted nuclear norm minimization for interference alignment

2013 IEEE International Conference on Communications (ICC), 2013
This paper considers a K-user multiple-input multiple-output (MIMO) interference channel in which uncoordinated interference appears. Due to the uncoordinated interference, perfect interference alignment (IA) may be not attained, which indicates the interference subspaces can not be completely aligned.
Huiqin Du, Tharm Ratnarajah, Mathini Sellathurai, Constantinos B. Papadias   +3 more
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Tensor Completion with BMD Factor Nuclear Norm Minimization [PDF]

CoRR
10 ...
Fan Tian, Mirjeta Pasha, Misha E. Kilmer, Eric L. Miller 0001, Abani Patra   +4 more
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INVERSE HALFTONING BASED ON WEIGHTED NUCLEAR NORM MINIMIZATION [PDF]

Journal of Mechanics in Medicine and Biology, 2021
The inverse halftoning technology refers to restoring a continuous-toned image from a halftoned image with only bi-level pixels. However, recovering the continuous images from their halftoned ones is normally ill-posed, which makes the inverse halftoning algorithm very challenging.
Jun Yang, Xiaojun Jia, Zihao Liu, Li Chen, Ying Wu   +4 more
openaire   +1 more source

Weighted Nuclear Norm Minimization on Multimodality Clustering

Security and Communication Networks, 2021
Generally, multimodality data contain different potential information available and are capable of providing an enhanced analytical result compared to monosource data. The way to combine the data plays a crucial role in multimodality data analysis which is worth investigating.
Lei Du, Songsong Dai, Haifeng Song 0002, Yuelong Chuang, Yingying Xu   +4 more
openaire   +1 more source

On Tensor Completion via Nuclear Norm Minimization [PDF]

Foundations of Computational Mathematics, 2015
Many problems can be formulated as recovering a low-rank tensor. Although an increasingly common task, tensor recovery remains a challenging problem because of the delicacy associated with the decomposition of higher order tensors. To overcome these difficulties, existing approaches often proceed by unfolding tensors into matrices and then apply ...
Ming Yuan 0001, Cun-Hui Zhang
openaire   +4 more sources