Results 221 to 230 of about 44,247 (253)

Low-rank matrix recovery with poison noise

2013 IEEE Global Conference on Signal and Information Processing, 2013
In this paper, we present a regularized maximum likelihood estimator to recover an approximately low-rank matrix under Poisson noise. We also establish performance bounds for the proposed estimator, by combining techniques for recovering sparse signals under Poisson noise [2], and methods for recovering low-rank matrices [3].
Yao Xie 0002, Yuejie Chi, A. Robert Calderbank   +2 more
openaire   +1 more source

Low-Rank Matrix Recovery for Topological Interference Management

2020 International Conference on Wireless Communications and Signal Processing (WCSP), 2020
Low-rank matrix completion plays an important role in modeling and computational methods for topological interference management (TIM), but in many applications affected by noise, these networks topological information cannot be fully directly observed, and one encounters the problem of recovering the topology information matrix given only incomplete ...
Xue Jiang 0003, Baoyu Zheng, Lei Wang 0009, Xiaoyun Hou   +3 more
openaire   +1 more source

Low-Rank Matrix Recovery with Discriminant Regularization

2013
Recently, image classification has been an active research topic due to the urgent need to retrieve and browse digital images via semantic keywords. Based on the success of low-rank matrix recovery which has been applied to statistical learning, computer vision and signal processing, this paper presents a novel low-rank matrix recovery algorithm with ...
Zhonglong Zheng, Haixin Zhang, Jiong Jia, Jianmin Zhao, Li Guo 0010, Fangmei Fu, Mudan Yu   +6 more
openaire   +1 more source

ELASTIC-NET REGULARIZATION FOR LOW-RANK MATRIX RECOVERY

International Journal of Wavelets, Multiresolution and Information Processing, 2012
This paper considers the problem of recovering a low-rank matrix from a small number of measurements consisting of linear combinations of the matrix entries. We extend the elastic-net regularization in compressive sensing to a more general setting, the matrix recovery setting, and consider the elastic-net regularization scheme for matrix recovery.
Hong Li 0009, Na Chen, Luoqing Li
openaire   +1 more source

Low-Rank Matrix Recovery for Electrical Capacitance Tomography

2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 2020
The permittivity distribution of two different dielectric materials may be reconstructed by electrical capacitance tomography (ECT). The reconstructed image, however, often has artifacts due to the effect of noise in measured capacitance data. To improve the image quality, a new approach based on low-rank matrix recovery is proposed for ECT to reduce ...
Jiamin Ye, Wuqiang Yang, Chao Wang 0019
openaire   +2 more sources

A focuss based method for low rank matrix recovery

2012 19th IEEE International Conference on Image Processing, 2012
In this work, we address the problem of low-rank matrix recovery from its under-sampled projections. The recovery is formulated as a Schatten-p norm minimization problem. We proposed a novel algorithm to solve the Schatten-p norm minimization problem based on the FOCUSS (FOCally Under-determined System Solver) approach.
Angshul Majumdar, Rabab Kreidieh Ward, Tyseer Aboulnasr   +2 more
openaire   +1 more source

Blocky artifact removal with low-rank matrix recovery

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014
In this paper, a novel image blocky artifact removal scheme based on low-rank matrix recovery is proposed. The problem of suppressing blocky artifacts is formulated as recovering a low-rank matrix from corrupted observations. During the deblocking processing, we do not directly recover the whole clean image but only its high-frequency component and ...
Ming Yin 0002, Junbin Gao, Yanfeng Sun, Shuting Cai   +3 more
openaire   +1 more source

Fisher Discrimination Based Low Rank Matrix Recovery

2013 2nd IAPR Asian Conference on Pattern Recognition, 2013
In this paper, we consider the issue of computing low rank (LR) recovery of matrices with sparse errors. Based on the success of low rank matrix recovery in statistical learning, computer vision and signal processing, a novel low rank matrix recovery algorithm with Fisher discrimination regularization (FDLR) is proposed.
Zhonglong Zheng, Mudan Yu, Jiong Jia, Huawen Liu, Haixin Zhang, Fangmei Fu, Xiaoqiao Huang   +6 more
openaire   +1 more source

Matrix Completion and Low-Rank Matrix Recovery

2013
This chapter is a natural development following Chap. 7. In other words, Chaps. 7 and 8 may be viewed as two parallel developments. In Chap. 7, compressed sensing exploits the sparsity structure in a vector, while low-rank matrix recovery—Chap. 8—exploits the low-rank structure of a matrix: sparse in the vector composed of singular values.
Robert Qiu, Michael Wicks
openaire   +1 more source

Decentralized Nonconvex Low-rank Matrix Recovery

IEEE Transactions on Image Processing
For the low-rank matrix recovery problem, algorithms that directly manipulate the low-rank matrix typically require computing the top singular values/vectors of the matrix and thus are computationally expensive. Matrix factorization is a computationally efficient nonconvex approach for low-rank matrix recovery, utilizing an alternating minimization or ...
Junzhuo Gao, Heng Lian
openaire   +2 more sources