Feature Selective Projection with Low-Rank Embedding and Dual Laplacian Regularization

Tang, Chang; Liu, Xinwang; Zhu, Xiaoyan; Xiong, Jian; Li, Miaomiao; Xia, Jingyuan; Wang, Xiangke; Wang, Lizhe

doi:10.1109/tkde.2019.2911946

Cited by 78 publications

(33 citation statements)

References 84 publications

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“…Instead of using all of the similarities in D S and T S , we extract the local neighbor information of drugs and targets. As to different drugs, the local neighbor similarity matrix

can be obtained from D S by

where π© ( d j ) denoted the neighbors of drug d j .In a similar way, the local neighbor similarity matrix

can be obtained from T S by

where π© ( t j ) denoted the neighbors of target t j .Since the iterations between drugs and targets are complex, different to previous graph regularized methods that only use the first-order connections to reflect the local pairwise proximity between vertices in a graph [ 45 β 48 ], we use the second-order connection to constrain that similar drugs should be connected with similar targets. Therefore, we have the following similarity affinity matrices calculating form:

where

and

represent the i th and j th column of original

, respectively, and

and

represent the i th and j th column of original

, respectively.…”

Section: Methodsmentioning

confidence: 99%

“…where N ðt j Þ denoted the neighbors of target t j .Since the iterations between drugs and targets are complex, different to previous graph regularized methods that only use the firstorder connections to reflect the local pairwise proximity between vertices in a graph [45][46][47][48], we use the secondorder connection to constrain that similar drugs should be connected with similar targets. Therefore, we have the following similarity affinity matrices calculating form:…”

Section: Problem Formulation Of Dti Predictionmentioning

confidence: 99%

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Drug-Target Interaction Prediction via Dual Laplacian Graph Regularized Logistic Matrix Factorization

Wang

2021

BioMed Research International

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Drug-target interactions provide useful information for biomedical drug discovery as well as drug development. However, it is costly and time consuming to find drug-target interactions by experimental methods. As a result, developing computational approaches for this task is necessary and has practical significance. In this study, we establish a novel dual Laplacian graph regularized logistic matrix factorization model for drug-target interaction prediction, referred to as DLGrLMF briefly. Specifically, DLGrLMF regards the task of drug-target interaction prediction as a weighted logistic matrix factorization problem, in which the experimentally validated interactions are allocated with larger weights. Meanwhile, by considering that drugs with similar chemical structure should have interactions with similar targets and targets with similar genomic sequence similarity should in turn have interactions with similar drugs, the drug pairwise chemical structure similarities as well as the target pairwise genomic sequence similarities are fully exploited to serve the matrix factorization problem by using a dual Laplacian graph regularization term. In addition, we design a gradient descent algorithm to solve the resultant optimization problem. Finally, the efficacy of DLGrLMF is validated on various benchmark datasets and the experimental results demonstrate that DLGrLMF performs better than other state-of-the-art methods. Case studies are also conducted to validate that DLGrLMF can successfully predict most of the experimental validated drug-target interactions.

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“…Instead of using all of the similarities in D S and T S , we extract the local neighbor information of drugs and targets. As to different drugs, the local neighbor similarity matrix

can be obtained from D S by

where π© ( d j ) denoted the neighbors of drug d j .In a similar way, the local neighbor similarity matrix

can be obtained from T S by

where

and

represent the i th and j th column of original

, respectively, and

and

represent the i th and j th column of original

, respectively.…”

Section: Methodsmentioning

confidence: 99%

Section: Problem Formulation Of Dti Predictionmentioning

confidence: 99%

Drug-Target Interaction Prediction via Dual Laplacian Graph Regularized Logistic Matrix Factorization

Wang

2021

BioMed Research International

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show abstract

“…As a result, the neighborhood-based methods tend to be sensitive to noise and data corruption. Various methods are designed to capture the underlying manifold structures of the data [30][31][32][33][34]. To construct a noise-resistant graph, the representation-based graph construction approach adopts the linear regression to generate a set of the edges and compute the corresponding weights.…”

Section: Related Workmentioning

confidence: 99%

Multi-Metric Induced Robust Graph

Zhang

Sun

et al. 2021

IEEE Access

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Graph-based learning model has a wide range of applications in machine learning and computer vision. The key issue of the graph-based applications is to construct an informative graph to effectively represent data correlations. In practice, real-world data is usually contaminated by complex noise beyond Gaussian noise and sparse noise, which degrades learning performance dramatically. To construct a robust graph that represents real-world data distribution well, we propose a novel graph construction method. The proposed method is designed to be robust to complex noise beyond sparse or Gaussian noise by combining different previously given metrics of measuring data errors, which robustly and correctly generates a set of edges. To demonstrate the superiority of the proposed method, we employed the clustering and classification tasks on the real-world datasets. The experimental results demonstrate that the proposed method is superior to the existing methods in both accuracy and robustness to complex noise.

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“…The three metrics are calculated in target local region, supposing that the target size is a × b and d is set to 20 as the neighborhood width. Furthermore, the probability of detection (P d ) and false alarm rate (F a ) are also very important indicators for wholly evaluating the detection performance, which are defined as F a = number of false detections number of images (29) In experiments, we deem that the detection of small target is correct under this case where there are pixels within a 5 × 5 window centering on the ground truth. A good detector owns high P d under low F a .…”

Section: Evaluation Indicatorsmentioning

confidence: 99%

Robust Infrared Small Target Detection via Jointly Sparse Constraint of l1/2-Metric and Dual-Graph Regularization

Zhou

Dai

et al. 2020

Remote Sensing

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Small target detection is a critical step in remotely infrared searching and guiding applications. However, previously proposed algorithms would exhibit performance deterioration in the presence of complex background. It is attributed to two main reasons. First, some common background interferences are difficult to eliminate effectively by using conventional sparse measure. Second, most methods only exploit the spatial information typically, but ignore the structural priors across feature space. To address these issues, this paper gives a novel model combining the spatial-feature graph regularization and l1/2-norm sparse constraint. In this model, the spatial and feature regularizations are imposed on the sparse component in the form of graph Laplacians, where the sparse component is enforced as the eigenvectors of their graph Laplacian matrices. Such an approach is to explore the geometric information in both data and feature space simultaneously. Moreover, l1/2-norm acts as a substitute of the traditional l1-norm to constrain the sparse component, further reducing the fake targets. Finally, an efficient optimization algorithm equipped with linearized alternating direction method with adaptive penalty (LADMAP) is carefully designed for model solution. Comprehensive experiments on different infrared scenes substantiate the superiority of the proposed method beyond 11 competitive algorithms in subjective and objective evaluation.

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Feature Selective Projection with Low-Rank Embedding and Dual Laplacian Regularization

Cited by 78 publications

References 84 publications

Drug-Target Interaction Prediction via Dual Laplacian Graph Regularized Logistic Matrix Factorization

Drug-Target Interaction Prediction via Dual Laplacian Graph Regularized Logistic Matrix Factorization

Multi-Metric Induced Robust Graph

Robust Infrared Small Target Detection via Jointly Sparse Constraint of l1/2-Metric and Dual-Graph Regularization

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