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Asystematic review of structured sparse learning Review

Lin-bo QIAO, Bo-feng ZHANG, Jin-shu SU, Xi-cheng LU

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 4,   Pages 445-463 doi: 10.1631/FITEE.1601489

Abstract: learning due to model parsimony and computational advantage.Structured sparse learning encodes the structural information of the variables and has been quite successfulThese regularizations have greatly improved the efficacy of sparse learning algorithms through the useIn this article, we present a systematic review of structured sparse learning including ideas, formulationslearning problems.

Keywords： Sparse learning     Structured sparse learning     Structured regularization    

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An assessment of surrogate fuel using Bayesian multiple kernel learning model in sight of sooting tendency

Frontiers in Energy 2022, Volume 16, Issue 2,   Pages 277-291 doi: 10.1007/s11708-021-0731-6

Abstract: novel predicting model of YSI values for surrogate fuels was proposed with the application of a machine learningmethod, named the Bayesian multiple kernel learning (BMKL) model.

Keywords： sooting tendency     yield sooting index     Bayesian multiple kernel learning     surrogate assessment     surrogate    

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Variational Bayesian multi-sparse component extraction for damage reconstruction of space debris hypervelocity Research Article

Xuegang HUANG, Anhua SHI, Qing LUO, Jinyang LUO,emei-126@126.com

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 4,   Pages 530-541 doi: 10.1631/FITEE.2000575

Abstract: To improve the survivability of orbiting spacecraft against space debris impacts, we propose an impact method. First, a multi-area damage mining model, which can describe damages in different spatial layers, is built based on an infrared thermal image sequence. Subsequently, to identify different impact damage types from infrared image data effectively, the inference is used to solve for the parameters in the model. Then, an image-processing framework is proposed to eliminate errors and compare locations of different damage types. It includes an image segmentation algorithm with an energy function and an image fusion method with . In the experiment, the proposed method is used to evaluate the complex damages caused by the impact of the secondary debris cloud on the rear wall of the typical Whipple shield configuration. Experimental results show that it can effectively identify and evaluate the complex damage caused by , including surface and internal defects.

Keywords： Hypervelocity impact     Variational Bayesian     Sparse representation     Damage assessment    

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Laplacian sparse dictionary learning for image classification based on sparse representation Article

Fang LI, Jia SHENG, San-yuan ZHANG

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 11,   Pages 1795-1805 doi: 10.1631/FITEE.1600039

Abstract: Sparse representation is a mathematical model for data representation that has proved to be a powerfultool for solving problems in various fields such as pattern recognition, machine learning, and computerAs one of the building blocks of the sparse representation method, dictionary learning plays an importantdictionary (LSD) learning.Our method is based on manifold learning and double sparsity.

Keywords： Sparse representation     Laplacian regularizer     Dictionary learning     Double sparsity     Manifold    

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Unsupervised feature selection via joint local learning and group sparse regression Regular Papers

Yue WU, Can WANG, Yue-qing ZHANG, Jia-jun BU

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 4,   Pages 538-553 doi: 10.1631/FITEE.1700804

Abstract: By selecting meaningful feature subsets, the performance of learning algorithms can be effectively improvedTo address this issue, we propose a novel unsupervised feature selection algorithm via joint local learningand group sparse regression (JLLGSR).JLLGSR incorporates local learning based clustering with group sparsity regularized regression in a singleformulation, and seeks features that respect both the manifold structure and group sparse structure

Keywords： Unsupervised     Local learning     Group sparse regression     Feature selection    

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Efficient scheme of low-dose CT reconstruction using TV minimization with an adaptive stopping strategy and sparsedictionary learning for post-processing Article

Yong DING, Tuo HU

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 12,   Pages 2001-2008 doi: 10.1631/FITEE.1700287

Abstract: imaging, we propose a promising reconstruction scheme which combines total-variation minimization and sparsedictionary learning to enhance the reconstruction performance, and properly schedule them with an adaptive

Keywords： Low-dose computed tomography (CT)     CT imaging     Total variation     Sparse dictionary learning    

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Application of machine learning algorithms for the evaluation of seismic soil liquefaction potential

Mahmood AHMAD, Xiao-Wei TANG, Jiang-Nan QIU, Feezan AHMAD, Wen-Jing GU

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 2,   Pages 490-505 doi: 10.1007/s11709-020-0669-5

Abstract: This study investigates the performance of four machine learning (ML) algorithms to evaluate the earthquake-inducedliquefaction potential of soil based on the cone penetration test field case history records using the Bayesianbelief network (BBN) learning software Netica.climbing (HC), tree augmented naive (TAN) Bayes, and Tabu search were adopted to perform parameter learning

Keywords： seismic soil liquefaction     Bayesian belief network     cone penetration test     parameter learning     structurallearning    

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A new constrained maximum margin approach to discriminative learning of Bayesian classifiers None

Ke GUO, Xia-bi LIU, Lun-hao GUO, Zong-jie LI, Zeng-min GENG

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 5,   Pages 639-650 doi: 10.1631/FITEE.1700007

Abstract: We propose a novel discriminative learning approach for Bayesian pattern classification, called ‘constrainedThe learning problem is to maximize the margin under the constraint that each training pattern is classifiedWe applied the proposed CMM approach to learn Bayesian classifiers based on Gaussian mixture models,

Keywords： Discriminative learning     Statistical modeling     Bayesian pattern classifiers     Gaussian mixture models     UCI    

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Robust object tracking with RGBD-based sparse learning Article

Zi-ang MA, Zhi-yu XIANG

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 7,   Pages 989-1001 doi: 10.1631/FITEE.1601338

Abstract: In this paper, a novel RGBD and sparse learning based tracker is proposed.The range data is integrated into the sparse learning framework in three respects.demonstrate that the proposed tracker outperforms the state-of-the-art tracking algorithms, including both sparselearning and RGBD based methods.

Keywords： Object tracking     Sparse learning     Depth view     Occlusion templates     Occlusion detection    

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Face recognition based on subset selection via metric learning on manifold

Hong SHAO,Shuang CHEN,Jie-yi ZHAO,Wen-cheng CUI,Tian-shu YU

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 12,   Pages 1046-1058 doi: 10.1631/FITEE.1500085

Abstract: With the development of face recognition using sparse representation based classification (SRC), manyHowever, when the dictionary is large and the representation is sparse, only a small proportion of theIn this paper, we employ a metric learning approach which helps find the active elements correctly by

Keywords： Face recognition     Sparse representation     Manifold structure     Metric learning     Subset selection    

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Multiple fault separation and detection by joint subspace learning for the health assessment of wind

Zhaohui DU, Xuefeng CHEN, Han ZHANG, Yanyang ZI, Ruqiang YAN

Frontiers of Mechanical Engineering 2017, Volume 12, Issue 3,   Pages 333-347 doi: 10.1007/s11465-017-0435-0

Abstract: Thus, this paper presents a joint subspace learning-based multiple fault detection (JSL-MFD) technique

Keywords： joint subspace learning     multiple fault diagnosis     sparse decomposition theory     coupling feature separation    

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Data-Driven Discovery of Stochastic Differential Equations Article

Yasen Wang, Huazhen Fang, Junyang Jin, Guijun Ma, Xin He, Xing Dai, Zuogong Yue, Cheng Cheng, Hai-Tao Zhang, Donglin Pu, Dongrui Wu, Ye Yuan, Jorge Gonçalves, Jürgen Kurths, Han Ding

Engineering 2022, Volume 17, Issue 10,   Pages 244-252 doi: 10.1016/j.eng.2022.02.007

Abstract: This study presents a novel framework for identifying SDEs by leveraging the sparse Bayesian learning

Keywords： Data-driven method     System identification     Sparse Bayesian learning     Stochastic differential equations    

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Data-Driven Model Falsification and Uncertainty Quantification for Fractured Reservoirs Article

Junling Fang, Bin Gong, Jef Caers

Engineering 2022, Volume 18, Issue 11,   Pages 116-128 doi: 10.1016/j.eng.2022.04.015

Abstract: Bayesian theorem provides a framework to quantify the uncertainty in geological modeling and flow simulationThe application of Bayesian methods to fractured reservoirs has mostly been limited to synthetic casesIn field applications, however, one of the main problems is that the Bayesian prior is falsified, becauseWe then employ an approximate Bayesian computation (ABC) method combined with a tree-based surrogate

Keywords： Bayesian evidential learning     Falsification     Fractured reservoir     Random forest     Approximate Bayesian computation    

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IN2CLOUD: A novel concept for collaborative management of big railway data

Jing LIN, Uday KUMAR

Frontiers of Engineering Management 2017, Volume 4, Issue 4,   Pages 428-436 doi: 10.15302/J-FEM-2017048

Abstract: sub-concepts, to address this challenge: 1) A hybrid cloud, 2) an intelligent cloud with hybrid cloud learning

Keywords： railway     intelligent asset management     collaborative learning     big data     hybrid cloud     Bayesian    

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Kernel sparse representation for MRI image analysis in automatic brain tumor segmentation None

Ji-jun TONG, Peng ZHANG, Yu-xiang WENG, Dan-hua ZHU

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 4,   Pages 471-480 doi: 10.1631/FITEE.1620342

Abstract: We propose a fully automatic brain tumor segmentation method based on kernel sparse coding.In this method, MRI images are pre-processed first to reduce the noise, and then kernel dictionary learningSparse coding is performed on the feature vectors extracted from the original MRI images, which are aA kernel-clustering algorithm based on dictionary learning is developed to code the voxels.

Keywords： Brain tumor segmentation     Kernel method     Sparse coding     Dictionary learning    

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Title Author Date Type Operation

Asystematic review of structured sparse learning

Lin-bo QIAO, Bo-feng ZHANG, Jin-shu SU, Xi-cheng LU

Journal Article

An assessment of surrogate fuel using Bayesian multiple kernel learning model in sight of sooting tendency

Journal Article

Variational Bayesian multi-sparse component extraction for damage reconstruction of space debris hypervelocity

Xuegang HUANG, Anhua SHI, Qing LUO, Jinyang LUO,emei-126@126.com

Journal Article

Laplacian sparse dictionary learning for image classification based on sparse representation

Fang LI, Jia SHENG, San-yuan ZHANG

Journal Article

Unsupervised feature selection via joint local learning and group sparse regression

Yue WU, Can WANG, Yue-qing ZHANG, Jia-jun BU

Journal Article

Efficient scheme of low-dose CT reconstruction using TV minimization with an adaptive stopping strategy and sparsedictionary learning for post-processing

Yong DING, Tuo HU

Journal Article

Application of machine learning algorithms for the evaluation of seismic soil liquefaction potential

Mahmood AHMAD, Xiao-Wei TANG, Jiang-Nan QIU, Feezan AHMAD, Wen-Jing GU

Journal Article

A new constrained maximum margin approach to discriminative learning of Bayesian classifiers

Ke GUO, Xia-bi LIU, Lun-hao GUO, Zong-jie LI, Zeng-min GENG

Journal Article

Robust object tracking with RGBD-based sparse learning

Zi-ang MA, Zhi-yu XIANG

Journal Article

Face recognition based on subset selection via metric learning on manifold

Hong SHAO,Shuang CHEN,Jie-yi ZHAO,Wen-cheng CUI,Tian-shu YU

Journal Article

Multiple fault separation and detection by joint subspace learning for the health assessment of wind

Zhaohui DU, Xuefeng CHEN, Han ZHANG, Yanyang ZI, Ruqiang YAN

Journal Article

Data-Driven Discovery of Stochastic Differential Equations

Yasen Wang, Huazhen Fang, Junyang Jin, Guijun Ma, Xin He, Xing Dai, Zuogong Yue, Cheng Cheng, Hai-Tao Zhang, Donglin Pu, Dongrui Wu, Ye Yuan, Jorge Gonçalves, Jürgen Kurths, Han Ding

Journal Article

Data-Driven Model Falsification and Uncertainty Quantification for Fractured Reservoirs

Junling Fang, Bin Gong, Jef Caers

Journal Article

IN2CLOUD: A novel concept for collaborative management of big railway data

Jing LIN, Uday KUMAR

Journal Article

Kernel sparse representation for MRI image analysis in automatic brain tumor segmentation

Ji-jun TONG, Peng ZHANG, Yu-xiang WENG, Dan-hua ZHU

Journal Article