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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: tool 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 importantLaplacian sparse dictionary (LSD) learning.Our method is based on manifold learning and double sparsity.An LSD is a sparse overcomplete dictionary that can preserve the intrinsic structure of the data and

Keywords： Sparse representation Laplacian regularizer Dictionary learning Double sparsity Manifold

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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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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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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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A Robust Transfer Dictionary Learning Algorithm for Industrial Process Monitoring Article

Chunhua Yang, Huiping Liang, Keke Huang, Yonggang Li, Weihua Gui

Engineering 2021, Volume 7, Issue 9, Pages 1262-1273 doi: 10.1016/j.eng.2020.08.028

Abstract: data and online testing data that is induced by changeable operation environments, a robust transfer dictionarylearning (RTDL) algorithm is proposed in this paper for industrial process monitoring.The RTDL is a synergy of representative learning and domain adaptive transfer learning.In this way, a robust dictionary can be learned even if the characteristics of the source domain andSuch a dictionary can effectively improve the performance of process monitoring and mode classification

Keywords： Process monitoring Multimode process Dictionary learning Transfer learning

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Multi-scale UDCT dictionary learning based highly undersampled MR image reconstruction using patch-based

Min YUAN,Bing-xin YANG,Yi-de MA,Jiu-wen ZHANG,Fu-xiang LU,Tong-feng ZHANG

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 12, Pages 1069-1087 doi: 10.1631/FITEE.1400423

Abstract: Recently, dictionary learning (DL) based methods have been introduced to compressed sensing magneticresonance imaging (CS-MRI), which outperforms pre-defined analytic sparse priors.However, single-scale trained dictionary directly from image patches is incapable of representing imageEach sub-dictionary of different sub-bands is trained independently to form the multi-scale dictionaries

Keywords： sensing (CS) Magnetic resonance imaging (MRI) Uniform discrete curvelet transform (UDCT) Multi-scale dictionarylearning (MSDL) Patch-based constraint splitting augmented Lagrangian shrinkage algorithm (PB C-SALSA

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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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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.Then, a special occlusion template set is designed to replenish the existing dictionary for handlingdemonstrate 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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Entity and relation extraction with rule-guided dictionary as domain knowledge

Frontiers of Engineering Management Pages 610-622 doi: 10.1007/s42524-022-0226-0

Abstract: Entity and relation extraction is an indispensable part of domain knowledge graph construction, which can serve relevant knowledge needs in a specific domain, such as providing support for product research, sales, risk control, and domain hotspot analysis. The existing entity and relation extraction methods that depend on pretrained models have shown promising performance on open datasets. However, the performance of these methods degrades when they face domain-specific datasets. Entity extraction models treat characters as basic semantic units while ignoring known character dependency in specific domains. Relation extraction is based on the hypothesis that the relations hidden in sentences are unified, thereby neglecting that relations may be diverse in different entity tuples. To address the problems above, this paper first introduced prior knowledge composed of domain dictionaries to enhance characters’ dependence. Second, domain rules were built to eliminate noise in entity relations and promote potential entity relation extraction. Finally, experiments were designed to verify the effectiveness of our proposed methods. Experimental results on two domains, including laser industry and unmanned ship, showed the superiority of our methods. The F1 value on laser industry entity, unmanned ship entity, laser industry relation, and unmanned ship relation datasets is improved by +1%, +6%, +2%, and +1%, respectively. In addition, the extraction accuracy of entity relation triplet reaches 83% and 76% on laser industry entity pair and unmanned ship entity pair datasets, respectively.

Keywords： entity extraction relation extraction prior knowledge domain rule

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Building trust networks in the absence of trust relations Article

Xin WANG, Ying WANG, Jian-hua GUO

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 10, Pages 1591-1600 doi: 10.1631/FITEE.1601341

Abstract: User-specified trust relations are often very sparse and dynamic, making them difficult to accuratelyIn this study, we investigate whether we can predict trust relations via a sparse learning model, and

Keywords： Trust network Sparse learning Homophily effect Interaction behaviors

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Sparse fast Clifford Fourier transform Article

Rui WANG, Yi-xuan ZHOU, Yan-liang JIN, Wen-ming CAO

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 8, Pages 1131-1141 doi: 10.1631/FITEE.1500452

Abstract: The sparse fast Fourier transform (sFFT) theory deals with the big data problem by using input data selectivelyThis has inspired us to create a new algorithm called sparse fast CFT (SFCFT), which can greatly improve

Keywords： Sparse fast Fourier transform (sFFT) Clifford Fourier transform (CFT) Sparse fast Clifford Fourier

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Non-convex sparse optimization-based impact force identification with limited vibration measurements

Frontiers of Mechanical Engineering 2023, Volume 18, Issue 3, doi: 10.1007/s11465-023-0762-2

Abstract: mn> regularization method often struggles to generate sparseMJX-TeXAtom-ORD">ℓ1 sparseTo alleviate such limitations, a novel non-convex sparse regularization method that uses the non-convexrealize simultaneous impact localization and time history reconstruction with an under-determined, sparse

Keywords： impact force identification inverse problem sparse regularization under-determined condition alternating

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Uncertainty propagation analysis by an extended sparse grid technique

X. Y. JIA, C. JIANG, C. M. FU, B. Y. NI, C. S. WANG, M. H. PING

Frontiers of Mechanical Engineering 2019, Volume 14, Issue 1, Pages 33-46 doi: 10.1007/s11465-018-0514-x

Abstract: In this paper, an uncertainty propagation analysis method is developed based on an extended sparse gridSubsequently, within the sparse grid numerical integration framework, the statistical moments of the

Keywords： uncertainty propagation analysis extended sparse grid maximum entropy principle extended Gauss integration

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Title Author Date Type Operation

Laplacian sparse dictionary learning for image classification based on sparse representation

Fang LI, Jia SHENG, San-yuan ZHANG

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

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

Asystematic review of structured sparse learning

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

Journal Article

A Robust Transfer Dictionary Learning Algorithm for Industrial Process Monitoring

Chunhua Yang, Huiping Liang, Keke Huang, Yonggang Li, Weihua Gui

Journal Article

Multi-scale UDCT dictionary learning based highly undersampled MR image reconstruction using patch-based