Resource Type

Journal Article 332

Year

2023 66

2022 69

2021 37

2020 38

2019 26

2018 17

2017 25

2016 10

2015 10

2014 1

2013 1

2011 1

2010 1

2009 1

2008 2

2007 3

2006 1

2005 1

2002 1

2001 3

open ︾

Keywords

Machine learning 50

Deep learning 36

machine learning 24

Reinforcement learning 15

deep learning 15

Artificial intelligence 14

artificial intelligence 5

Active learning 4

artificial neural network 4

Attention 3

Autonomous driving 3

Bayesian optimization 3

Big data 3

Sparse representation 3

Adaptive dynamic programming 2

Additive manufacturing 2

Adversarial attack 2

Autonomous learning 2

Autonomous vehicle 2

open ︾

Search scope:

排序： Display mode:

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    

HTML PDF Collect

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    

HTML PDF Collect

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    

HTML PDF Collect

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    

HTML PDF Collect

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    

HTML PDF Collect

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    

HTML PDF Collect

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    

HTML PDF Collect

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    

HTML PDF Collect

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    

HTML PDF Collect

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    

HTML PDF Collect

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    

HTML PDF Collect

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    

HTML PDF Collect

Home location inference from sparse and noisy data: models and applications

Tian-ran HU,Jie-bo LUO,Henry KAUTZ,Adam SADILEK

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 5,   Pages 389-402 doi: 10.1631/FITEE.1500385

Abstract: In particular, the sparse and noisy nature of social media data poses serious challenges in pinpointingknowledge, this is the first time home location has been detected at such a fine granularity using sparse

Keywords： Home location     Mobility patterns     Healthcare    

HTML PDF Collect

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    

HTML PDF Collect

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    

HTML PDF Collect

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

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

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

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

Building trust networks in the absence of trust relations

Xin WANG, Ying WANG, Jian-hua GUO

Journal Article

Sparse fast Clifford Fourier transform

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

Journal Article

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

Journal Article

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

Journal Article

Home location inference from sparse and noisy data: models and applications

Tian-ran HU,Jie-bo LUO,Henry KAUTZ,Adam SADILEK

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

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