Foreground-Background Separation via Generalized Nuclear Norm and Structured Sparse Norm Based Low-Rank and Sparse Decomposition

Yang, Yongpeng; Yang, Zhenzhen; Li, Jianlin; Lü, Fan

doi:10.1109/access.2020.2992132

Cited by 13 publications

(5 citation statements)

References 41 publications

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“…For example, �X� * = i σ i is the nuclear norm which is the sum of its SV that σ i represents the i-th SV of the matrix X. NNM attempts to recover matrix X, actual low rank, by minimizing �X� * from degraded observation matrix Y. In recent years, NNM-based methods have been used in many applications such as video denoising [60], background extraction [61], data recovery [62] and subspace clustering [63,64]. The matrix rank can be recovered under the conditions of the limited and theoretic warranty.…”

Section: Methodsmentioning

confidence: 99%

Drug–target interaction prediction using unifying of graph regularized nuclear norm with bilinear factorization

et al. 2021

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Background Wet-lab experiments for identification of interactions between drugs and target proteins are time-consuming, costly and labor-intensive. The use of computational prediction of drug–target interactions (DTIs), which is one of the significant points in drug discovery, has been considered by many researchers in recent years. It also reduces the search space of interactions by proposing potential interaction candidates. Results In this paper, a new approach based on unifying matrix factorization and nuclear norm minimization is proposed to find a low-rank interaction. In this combined method, to solve the low-rank matrix approximation, the terms in the DTI problem are used in such a way that the nuclear norm regularized problem is optimized by a bilinear factorization based on Rank-Restricted Soft Singular Value Decomposition (RRSSVD). In the proposed method, adjacencies between drugs and targets are encoded by graphs. Drug–target interaction, drug-drug similarity, target-target, and combination of similarities have also been used as input. Conclusions The proposed method is evaluated on four benchmark datasets known as Enzymes (E), Ion channels (ICs), G protein-coupled receptors (GPCRs) and nuclear receptors (NRs) based on AUC, AUPR, and time measure. The results show an improvement in the performance of the proposed method compared to the state-of-the-art techniques.

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Section: Methodsmentioning

confidence: 99%

Drug–target interaction prediction using unifying of graph regularized nuclear norm with bilinear factorization

et al. 2021

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“…Equation ( 3) can be solved by applying a norm threshold action on singular values of the observation matrix in the form of Equation ( 4) [11,12]:…”

Section: Proposed Methodsmentioning

confidence: 99%

Prediction of Drug-Target Protein Interaction Based on the Minimization of Weighted Nuclear Norm and Similarity Graph between Drugs and Target Proteins

Sorkhi

Hashemi

Iranpour

et al. 2021

IJE

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Identification of drug-target protein interaction plays an important role in the drug discovery process. Given the fact that prediction experiments are time-consuming, tedious, and very costly, the computational prediction could be a proper solution for decreasing search space for evaluation of the interaction between drug and target. In this paper, a novel approach based on the known drug-target interactions based on similarity graphs is proposed. It was shown that use of this method was a lowranking issue and WNNM (weighted nuclear norm minimization) method was applied to detect the drug-target interactions. In the proposed method, the interaction between the drug and the target is encoded by graphs. Also known drug-target interaction, drug-drug similarity, target-target and combination of similarities were used as input. The proposed method was performed on four benchmark datasets, including enzymes (Es), ion channels (IC), G protein-coupled receptors (GPCRs), and nuclear receptors (NRs) based on the AUC and AUPR criteria. Finally, the results showed the improved performance of the proposed method.

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“…The kernel norm is a good convex proxy for the minimization of rank functions [10]. Yang et al [11] proved that the kernel norm is the most compact convex lower bound of the rank function. And the relationship between kernel norm and matrix rank is similar to that between the L1 norm and l0 norm of the vector.…”

Section: Introductionmentioning

confidence: 99%

Quantization and application of low-rank tensor decomposition based on the deep learning model.

Zhao¹

2023

3C TIC

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Watching the presentation of a large-scale network is very important for network state tracking, performance optimization, traffic engineering, anomaly detection, fault analysis, etc. In this paper, we try to develop deep learning technology to solve the defect problem of tensor filling based on inner product interaction. To solve the limitations of the existing tensor-filling algorithms, a new neural tensor-filling (NTC) model is proposed. NTC model can effectively type the third-order communication between data landscapes through outer creation operation. It creates the third-order interaction mapping tensor. On this basis, the interaction between local features of the 3D neural network is studied. In this paper, another fusion neural tensor filling (Fu NTC) model is proposed to solve the problem that the NTC model can only extract the nonlinear complex structural information between potential feature dimensions. In the framework of the neural network, the NTC model and tensor decomposition model share the same potential feature embedding. It can effectively extract nonlinear feature information and linear feature information at the same time. It achieves higher precision data recovery.

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Foreground-Background Separation via Generalized Nuclear Norm and Structured Sparse Norm Based Low-Rank and Sparse Decomposition

Cited by 13 publications

References 41 publications

Drug–target interaction prediction using unifying of graph regularized nuclear norm with bilinear factorization

Drug–target interaction prediction using unifying of graph regularized nuclear norm with bilinear factorization

Prediction of Drug-Target Protein Interaction Based on the Minimization of Weighted Nuclear Norm and Similarity Graph between Drugs and Target Proteins

Quantization and application of low-rank tensor decomposition based on the deep learning model.

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