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< Page ,Total 47 >
Efficient projected gradient methods for cardinality constrained optimization Scopus CSCD SCIE
期刊论文 | 2019 , 62 (2) , 245-268 | Science China Mathematics
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Abstract :

© 2018 Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature Sparse optimization has attracted increasing attention in numerous areas such as compressed sensing, financial optimization and image processing. In this paper, we first consider a special class of cardinality constrained optimization problems, which involves box constraints and a singly linear constraint. An effcient approach is provided for calculating the projection over the feasibility set after a careful analysis on the projection subproblem. Then we present several types of projected gradient methods for a general class of cardinality constrained optimization problems. Global convergence of the methods are established under suitable assumptions. Finally, we illustrate some applications of the proposed methods for signal recovery and index tracking. Especially for index tracking, we propose a new model subject to an adaptive upper bound on the sparse portfolio weights. The computational results demonstrate that the proposed projected gradient methods are effcient in terms of solution quality.

Keyword :

global convergence index tracking projected gradient method signal recovery sparse approximation

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GB/T 7714 Xu, Fengmin , Dai, Yuhong , Zhao, Zhihu et al. Efficient projected gradient methods for cardinality constrained optimization [J]. | Science China Mathematics , 2019 , 62 (2) : 245-268 .
MLA Xu, Fengmin et al. "Efficient projected gradient methods for cardinality constrained optimization" . | Science China Mathematics 62 . 2 (2019) : 245-268 .
APA Xu, Fengmin , Dai, Yuhong , Zhao, Zhihu , Xu, Zongben . Efficient projected gradient methods for cardinality constrained optimization . | Science China Mathematics , 2019 , 62 (2) , 245-268 .
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Optimizing a Parameterized Plug-and-Play ADMM for Iterative Low-Dose CT Reconstruction Scopus SCIE
期刊论文 | 2019 , 38 (2) , 371-382 | IEEE Transactions on Medical Imaging
WoS CC Cited Count: 1 SCOPUS Cited Count: 4
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IEEE Reducing the exposure to X-ray radiation while maintaining a clinically acceptable image quality is desirable in various CT applications. To realize low-dose CT (LdCT) imaging, model-based iterative reconstruction (MBIR) algorithms are widely adopted, but they require proper prior knowledge assumptions in the sinogram and/or image domains and involve tedious manual optimization of multiple parameters. In this work, we propose a deep learning (DL)-based strategy for MBIR to simultaneously address prior knowledge design and MBIR parameter selection in one optimization framework. Specifically, a parameterized plug-and-play alternating direction method of multipliers (3pADMM) is proposed for the general penalized weighted least-squares (PWLS) model, and then, by adopting the basic idea of DL, the parameterized plug-and-play (3p) prior and the related parameters are optimized simultaneously in a single framework using a large number of training data. The main contribution of this work is that the 3p prior and the related parameters in the proposed 3pADMM framework can be supervised and optimized simultaneously to achieve robust LdCT reconstruction performance. Experimental results obtained on clinical patient datasets demonstrate that the proposed method can achieve promising gains over existing algorithms for LdCT image reconstruction in terms of noise-induced artifact suppression and edge detail preservation.

Keyword :

Biomedical imaging Computed tomography deep learning Image reconstruction low-dose CT Machine learning Optimization parameterized plug-and-play ADMM X-ray imaging

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GB/T 7714 He, Ji , Yang, Yan , Wang, Yongbo et al. Optimizing a Parameterized Plug-and-Play ADMM for Iterative Low-Dose CT Reconstruction [J]. | IEEE Transactions on Medical Imaging , 2019 , 38 (2) : 371-382 .
MLA He, Ji et al. "Optimizing a Parameterized Plug-and-Play ADMM for Iterative Low-Dose CT Reconstruction" . | IEEE Transactions on Medical Imaging 38 . 2 (2019) : 371-382 .
APA He, Ji , Yang, Yan , Wang, Yongbo , Zeng, Dong , Bian, Zhaoying , Zhang, Hao et al. Optimizing a Parameterized Plug-and-Play ADMM for Iterative Low-Dose CT Reconstruction . | IEEE Transactions on Medical Imaging , 2019 , 38 (2) , 371-382 .
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An Efficient Iterative Cerebral Perfusion CT Reconstruction via Low-Rank Tensor Decomposition With Spatial-Temporal Total Variation Regularization SCIE
期刊论文 | 2019 , 38 (2) , 360-370 | IEEE TRANSACTIONS ON MEDICAL IMAGING
WoS CC Cited Count: 2
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Cerebrovascular diseases, i.e., acute stroke, are a common cause of serious long-term disability. Cerebral perfusion computed tomography (CPCT) can provide rapid, high-resolution, quantitative hemodynamic maps to assess and stratify perfusion in patients with acute stroke symptoms. However, CPCT imaging typically involves a substantial radiation dose due to its repeated scanning protocol. Therefore, in this paper, we present a low-dose CPCT image reconstruction method to yield high-quality CPCT images and high-precision hemodynamic maps by utilizing the great similarity information among the repeated scanned CPCT images. Specifically, a newly developed low-rank tensor decomposition with spatial-temporal total variation (LRTD-STTV) regularization is incorporated into the reconstruction model. In the LRTD-STTV regularization, the tensor Tucker decomposition is used to describe global spatial-temporal correlations hidden in the sequential CPCT images, and it is superior to the matricization model (i.e., low-rank model) that fails to fully investigate the prior knowledge of the intrinsic structures of the CPCT images after vectorizing the CPCT images. Moreover, the spatial-temporal TV regularization is used to characterize the local piecewise smooth structure in the spatial domain and the pixels' similarity with the adjacent frames in the temporal domain, because the intensity at each pixel in CPCT images is similar to its neighbors. Therefore, the presented LRTD-STTV model can efficiently deliver faithful underlying information of the CPCT images and preserve the spatial structures. An efficient alternating direction method of multipliers algorithm is also developed to solve the presented LRTD-STTV model. Extensive experimental results on numerical phantom and patient data are clearly demonstrated that the presented model can significantly improve the quality of CPCT images and provide accurate diagnostic features in hemodynamic maps for low-dose cases compared with the existing popular algorithms.

Keyword :

low-rank Computed tomography tensor regularization cerebral perfusion

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GB/T 7714 Li, Sui , Zeng, Dong , Peng, Jiangjun et al. An Efficient Iterative Cerebral Perfusion CT Reconstruction via Low-Rank Tensor Decomposition With Spatial-Temporal Total Variation Regularization [J]. | IEEE TRANSACTIONS ON MEDICAL IMAGING , 2019 , 38 (2) : 360-370 .
MLA Li, Sui et al. "An Efficient Iterative Cerebral Perfusion CT Reconstruction via Low-Rank Tensor Decomposition With Spatial-Temporal Total Variation Regularization" . | IEEE TRANSACTIONS ON MEDICAL IMAGING 38 . 2 (2019) : 360-370 .
APA Li, Sui , Zeng, Dong , Peng, Jiangjun , Bian, Zhaoying , Zhang, Hao , Xie, Qi et al. An Efficient Iterative Cerebral Perfusion CT Reconstruction via Low-Rank Tensor Decomposition With Spatial-Temporal Total Variation Regularization . | IEEE TRANSACTIONS ON MEDICAL IMAGING , 2019 , 38 (2) , 360-370 .
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A Graph-Based Semisupervised Deep Learning Model for PolSAR Image Classification EI Scopus SCIE
期刊论文 | 2019 , 57 (4) , 2116-2132 | IEEE Transactions on Geoscience and Remote Sensing
SCOPUS Cited Count: 3
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Abstract :

Aiming at improving the classification accuracy with limited numbers of labeled pixels in polarimetric synthetic aperture radar (PolSAR) image classification task, this paper presents a graph-based semisupervised deep learning model for PolSAR image classification. It models the PolSAR image as an undirected graph, where the nodes correspond to the labeled and unlabeled pixels, and the weighted edges represent similarities between the pixels. Upon the graph, we design an energy function incorporating a semisupervision term, a convolutional neural network (CNN) term, and a pairwise smoothness term. The employed CNN extracts abstract and data-driven polarimetric features and outputs class label predictions to the graph model. The semisupervision term enforces the category label constraints on the human-labeled pixels. The pairwise smoothness term encourages class label smoothness and the alignment of class label boundaries with the image edges. Starting from an initialized class label map generated based on K-Wishart distribution hypothesis or superpixel segmentation of PauliRGB images, we iteratively and alternately optimize the defined energy function until it converges. We conducted experiments on two real benchmark PolSAR images, and extensive experiments demonstrated that our approach achieved the state-of-the-art results for PolSAR image classification. IEEE

Keyword :

Convolutional Neural Networks (CNN) Graph model Image edge detection Polarimetric synthetic aperture radars Prediction algorithms Semi-supervised method Task analysis

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GB/T 7714 Bi, Haixia , Sun, Jian , Xu, Zongben . A Graph-Based Semisupervised Deep Learning Model for PolSAR Image Classification [J]. | IEEE Transactions on Geoscience and Remote Sensing , 2019 , 57 (4) : 2116-2132 .
MLA Bi, Haixia et al. "A Graph-Based Semisupervised Deep Learning Model for PolSAR Image Classification" . | IEEE Transactions on Geoscience and Remote Sensing 57 . 4 (2019) : 2116-2132 .
APA Bi, Haixia , Sun, Jian , Xu, Zongben . A Graph-Based Semisupervised Deep Learning Model for PolSAR Image Classification . | IEEE Transactions on Geoscience and Remote Sensing , 2019 , 57 (4) , 2116-2132 .
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Preface EI
期刊论文 | 2018 , 945 , V | Communications in Computer and Information Science
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GB/T 7714 Xu, Zongben , Gao, Xinbo , Miao, Qiguang et al. Preface [J]. | Communications in Computer and Information Science , 2018 , 945 : V .
MLA Xu, Zongben et al. "Preface" . | Communications in Computer and Information Science 945 (2018) : V .
APA Xu, Zongben , Gao, Xinbo , Miao, Qiguang , Zhang, Yunquan , Bu, Jiajun . Preface . | Communications in Computer and Information Science , 2018 , 945 , V .
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Greedy Criterion in Orthogonal Greedy Learning EI SCIE Scopus
期刊论文 | 2018 , 48 (3) , 955-966 | IEEE TRANSACTIONS ON CYBERNETICS
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Abstract :

Orthogonal greedy learning (OGL) is a stepwise learning scheme that starts with selecting a new atom from a specified dictionary via the steepest gradient descent (SGD) and then builds the estimator through orthogonal projection. In this paper, we found that SGD is not the unique greedy criterion and introduced a new greedy criterion, called as "delta-greedy threshold" for learning. Based on this new greedy criterion, we derived a straightforward termination rule for OGL. Our theoretical study shows that the new learning scheme can achieve the existing (almost) optimal learning rate of OGL. Numerical experiments are also provided to support that this new scheme can achieve almost optimal generalization performance while requiring less computation than OGL.

Keyword :

Generalization performance orthogonal greedy learning (OGL) supervised learning greedy criterion greedy algorithms

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GB/T 7714 Xu, Lin , Lin, Shaobo , Zeng, Jinshan et al. Greedy Criterion in Orthogonal Greedy Learning [J]. | IEEE TRANSACTIONS ON CYBERNETICS , 2018 , 48 (3) : 955-966 .
MLA Xu, Lin et al. "Greedy Criterion in Orthogonal Greedy Learning" . | IEEE TRANSACTIONS ON CYBERNETICS 48 . 3 (2018) : 955-966 .
APA Xu, Lin , Lin, Shaobo , Zeng, Jinshan , Liu, Xia , Fang, Yi , Xu, Zongben . Greedy Criterion in Orthogonal Greedy Learning . | IEEE TRANSACTIONS ON CYBERNETICS , 2018 , 48 (3) , 955-966 .
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Learning Through Deterministic Assignment of Hidden Parameters EI
期刊论文 | 2018 | IEEE Transactions on Cybernetics
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Supervised learning frequently boils down to determining hidden and bright parameters in a parameterized hypothesis space based on finite input-output samples. The hidden parameters determine the nonlinear mechanism of an estimator, while the bright parameters characterize the linear mechanism. In a traditional learning paradigm, hidden and bright parameters are not distinguished and trained simultaneously in one learning process. Such a one-stage learning (OSL) brings a benefit of theoretical analysis but suffers from the high computational burden. In this paper, we propose a two-stage learning scheme, learning through deterministic assignment of hidden parameters (LtDaHPs), suggesting to deterministically generate the hidden parameters by using minimal Riesz energy points on a sphere and equally spaced points in an interval. We theoretically show that with such a deterministic assignment of hidden parameters, LtDaHP with a neural network realization almost shares the same generalization performance with that of OSL. Then, LtDaHP provides an effective way to overcome the high computational burden of OSL. We present a series of simulations and application examples to support the outperformance of LtDaHP. IEEE

Keyword :

Application examples Bright parameters Generalization performance hidden parameters Learning rates Nonlinear mechanisms Traditional learning Uncertainty

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GB/T 7714 Fang, Jian , Lin, Shaobo , Xu, Zongben . Learning Through Deterministic Assignment of Hidden Parameters [J]. | IEEE Transactions on Cybernetics , 2018 .
MLA Fang, Jian et al. "Learning Through Deterministic Assignment of Hidden Parameters" . | IEEE Transactions on Cybernetics (2018) .
APA Fang, Jian , Lin, Shaobo , Xu, Zongben . Learning Through Deterministic Assignment of Hidden Parameters . | IEEE Transactions on Cybernetics , 2018 .
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Joint Analysis of Individual-level and Summary-level GWAS Data by Leveraging Pleiotropy. PubMed
期刊论文 | 2018 | Bioinformatics
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A large number of recent genome-wide association studies (GWASs) for complex phenotypes confirm the early conjecture for polygenicity, suggesting the presence of large number of variants with only tiny or moderate effects. However, due to the limited sample size of a single GWAS, many associated genetic variants are too weak to achieve the genome-wide significance. These undiscovered variants further limit the prediction capability of GWAS. Restricted access to the individual-level data and the increasing availability of the published GWAS results motivate the development of methods integrating both the individual-level and summary-level data. How to build the connection between the individual-level and summary-level data determines the efficiency of using the existing abundant summary-level resources with limited individual-level data, and this issue inspires more efforts in the existing area.In this study, we propose a novel statistical approach, LEP, which provides a novel way of modeling the connection between the individual-level data and summary-level data. LEP integrates both types of data by LEveraing Pleiotropy to increase the statistical power of risk variants identification and the accuracy of risk prediction. The algorithm for parameter estimation is developed to handle genome-wide-scale data. Through comprehensive simulation studies, we demonstrated the advantages of LEP over the existing methods. We further applied LEP to perform integrative analysis of Crohn's disease from WTCCC and summary statistics from GWAS of some other diseases, such as Type 1 diabetes, Ulcerative colitis and Primary biliary cirrhosis. LEP was able to significantly increase the statistical power of identifying risk variants and improve the risk prediction accuracy from 63.39% (± 0.58%) to 68.33% (± 0.32%) using about 195,000 variants.The LEP software is available at https://github.com/daviddaigithub/LEP.

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GB/T 7714 Dai Mingwei , Wan Xiang , Peng Hao et al. Joint Analysis of Individual-level and Summary-level GWAS Data by Leveraging Pleiotropy. [J]. | Bioinformatics , 2018 .
MLA Dai Mingwei et al. "Joint Analysis of Individual-level and Summary-level GWAS Data by Leveraging Pleiotropy." . | Bioinformatics (2018) .
APA Dai Mingwei , Wan Xiang , Peng Hao , Wang Yao , Liu Yue , Liu Jin et al. Joint Analysis of Individual-level and Summary-level GWAS Data by Leveraging Pleiotropy. . | Bioinformatics , 2018 .
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Convergence of multi-block Bregman ADMM for nonconvex composite problems EI SCIE Scopus CSCD
期刊论文 | 2018 , 61 (12) | SCIENCE CHINA-INFORMATION SCIENCES
WoS CC Cited Count: 6 SCOPUS Cited Count: 6
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Abstract :

The alternating direction method with multipliers (ADMM) is one of the most powerful and successful methods for solving various composite problems. The convergence of the conventional ADMM (i.e., 2-block) for convex objective functions has been stated for a long time, and its convergence for nonconvex objective functions has, however, been established very recently. The multi-block ADMM, a natural extension of ADMM, is a widely used scheme and has also been found very useful in solving various nonconvex optimization problems. It is thus expected to establish the convergence of the multi-block ADMM under nonconvex frameworks. In this paper, we first justify the convergence of 3-block Bregman ADMM. We next extend these results to the N-block case (N >= 3), which underlines the feasibility of multi-block ADMM applications in nonconvex settings. Finally, we present a simulation study and a real-world application to support the correctness of the obtained theoretical assertions.

Keyword :

subanalytic function Bregman distance alternating direction method nonconvex regularization K-L inequality

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GB/T 7714 Wang, Fenghui , Cao, Wenfei , Xu, Zongben . Convergence of multi-block Bregman ADMM for nonconvex composite problems [J]. | SCIENCE CHINA-INFORMATION SCIENCES , 2018 , 61 (12) .
MLA Wang, Fenghui et al. "Convergence of multi-block Bregman ADMM for nonconvex composite problems" . | SCIENCE CHINA-INFORMATION SCIENCES 61 . 12 (2018) .
APA Wang, Fenghui , Cao, Wenfei , Xu, Zongben . Convergence of multi-block Bregman ADMM for nonconvex composite problems . | SCIENCE CHINA-INFORMATION SCIENCES , 2018 , 61 (12) .
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Hyperspectral Image Classification With Markov Random Fields and a Convolutional Neural Network EI SCIE Scopus
期刊论文 | 2018 , 27 (5) , 2354-2367 | IEEE TRANSACTIONS ON IMAGE PROCESSING
WoS CC Cited Count: 19 SCOPUS Cited Count: 23
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This paper presents a new supervised classification algorithm for remotely sensed hyperspectral image (HSI) which integrates spectral and spatial information in a unified Bayesian framework. First, we formulate the HSI classification problem from a Bayesian perspective. Then, we adopt a convolutional neural network (CNN) to learn the posterior class distributions using a patch-wise training strategy to better use the spatial information. Next, spatial information is further considered by placing a spatial smoothness prior on the labels. Finally, we iteratively update the CNN parameters using stochastic gradient decent and update the class labels of all pixel vectors using alpha-expansion min-cut-based algorithm. Compared with the other state-of-the-art methods, the classification method achieves better performance on one synthetic data set and two benchmark HSI data sets in a number of experimental settings.

Keyword :

Hyperspectral image classification Markov random fields convolutional neural networks

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GB/T 7714 Cao, Xiangyong , Zhou, Feng , Xu, Lin et al. Hyperspectral Image Classification With Markov Random Fields and a Convolutional Neural Network [J]. | IEEE TRANSACTIONS ON IMAGE PROCESSING , 2018 , 27 (5) : 2354-2367 .
MLA Cao, Xiangyong et al. "Hyperspectral Image Classification With Markov Random Fields and a Convolutional Neural Network" . | IEEE TRANSACTIONS ON IMAGE PROCESSING 27 . 5 (2018) : 2354-2367 .
APA Cao, Xiangyong , Zhou, Feng , Xu, Lin , Meng, Deyu , Xu, Zongben , Paisley, John . Hyperspectral Image Classification With Markov Random Fields and a Convolutional Neural Network . | IEEE TRANSACTIONS ON IMAGE PROCESSING , 2018 , 27 (5) , 2354-2367 .
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