Results 1 to 10 of about 256,120 (256)
Repairing Artifacts in Neural Activity Recordings Using Low-Rank Matrix Estimation [PDF]
Sensors, 2023 Electrophysiology recordings are frequently affected by artifacts (e.g., subject motion or eye movements), which reduces the number of available trials and affects the statistical power.
Shruti Naik +2 more
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A Gradient System for Low Rank Matrix Completion [PDF]
Axioms, 2018 In this article we present and discuss a two step methodology to find the closest low rank completion of a sparse large matrix. Given a large sparse matrix M, the method consists of fixing the rank to r and then looking for the closest rank-r matrix X to
Carmela Scalone, Nicola Guglielmi
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Lower bounds for the low-rank matrix approximation [PDF]
Journal of Inequalities and Applications, 2017 Low-rank matrix recovery is an active topic drawing the attention of many researchers. It addresses the problem of approximating the observed data matrix by an unknown low-rank matrix. Suppose that A is a low-rank matrix approximation of D, where D and A
Jicheng Li, Zisheng Liu, Guo Li
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Proximal iteratively reweighted algorithm for low-rank matrix recovery [PDF]
Journal of Inequalities and Applications, 2018 This paper proposes a proximal iteratively reweighted algorithm to recover a low-rank matrix based on the weighted fixed point method. The weighted singular value thresholding problem gains a closed form solution because of the special properties of ...
Chao-Qun Ma, Yi-Shuai Ren
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Beyond Low Rank + Sparse: Multi-scale Low Rank Matrix Decomposition. [PDF]
IEEE J Sel Top Signal Process, 2016 We present a natural generalization of the recent low rank + sparse matrix decomposition and consider the decomposition of matrices into components of multiple scales. Such decomposition is well motivated in practice as data matrices often exhibit local correlations in multiple scales.
Ong F, Lustig M.
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Clustering-based low-rank matrix approximation for multimodal medical image compression [PDF]
BioData MiningMedical images are inherently high-resolution and contain locally varying structures that are crucial for diagnosis. Efficient compression of such data must therefore preserve diagnostic fidelity while minimizing redundancy. Low-rank matrix approximation
Sisipho Hamlomo, Marcellin Atemkeng
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A new method based on the manifold-alternative approximating for low-rank matrix completion [PDF]
Journal of Inequalities and Applications, 2018 In this paper, a new method is proposed for low-rank matrix completion which is based on the least squares approximating to the known elements in the manifold formed by the singular vectors of the partial singular value decomposition alternatively.
Fujiao Ren, Ruiping Wen
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Improved low-rank matrix recovery method for predicting miRNA-disease association [PDF]
Scientific Reports, 2017 MicroRNAs (miRNAs) performs crucial roles in various human diseases, but miRNA-related pathogenic mechanisms remain incompletely understood. Revealing the potential relationship between miRNAs and diseases is a critical problem in biomedical research ...
Li Peng +5 more
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Low-rank matrix approximations over canonical subspaces
Journal of Numerical Analysis and Approximation Theory, 2020 In this paper we derive closed form expressions for the nearest rank-\(k\) matrix on canonical subspaces. We start by studying three kinds of subspaces. Let \(X\) and \(Y\) be a pair of given matrices. The first subspace contains all the \(m\times
Achiya Dax
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Bounded Matrix Low Rank Approximation [PDF]
2012 IEEE 12th International Conference on Data Mining, 2012 Matrix lower rank approximations such as non-negative matrix factorization (NMF) have been successfully used to solve many data mining tasks. In this paper, we propose a new matrix lower rank approximation called Bounded Matrix Low Rank Approximation (BMA) which imposes a lower and an upper bound on every element of a lower rank matrix that best ...
Ramakrishnan Kannan +2 more
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