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Speech emotion recognitionwith unsupervised feature learning

Zheng-wei HUANG,Wen-tao XUE,Qi-rong MAO

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 5,   Pages 358-366 doi: 10.1631/FITEE.1400323

Abstract: In this paper, we apply several unsupervised feature learning algorithms (including -means clustering, the sparse auto-encoder, and sparse restricted Boltzmann machines), which have promise for learning

Keywords： Speech emotion recognition     Unsupervised feature learning     Neural network     Affect computing    

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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 improvedBecause label information is expensive to obtain, unsupervised feature selection methods are more widelyThe key to unsupervised feature selection is to find features that effectively reflect the underlyingTo address this issue, we propose a novel unsupervised feature selection algorithm via joint local learningJLLGSR incorporates local learning based clustering with group sparsity regularized regression in a single

Keywords： Unsupervised     Local learning     Group sparse regression     Feature selection    

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Imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph feature learning

Frontiers of Mechanical Engineering 2021, Volume 16, Issue 4,   Pages 829-839 doi: 10.1007/s11465-021-0652-4

Abstract: this problem, an imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph featurelearning is proposed in this paper.Unsupervised autoencoder is firstly used to compress every monitoring signal into a low-dimensional vectoreffectively achieve rotating machinery fault diagnosis towards imbalanced training dataset through graph featurelearning.

Keywords： imbalanced fault diagnosis     graph feature learning     rotating machinery     autoencoder    

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Unknown fault detection for EGT multi-temperature signals based on self-supervised feature learning and

Frontiers in Energy 2023, Volume 17, Issue 4,   Pages 527-544 doi: 10.1007/s11708-023-0880-x

Abstract: Data-based methods of supervised learning have gained popularity because of available Big Data and computingTherefore, a fault detection method based on self-supervised feature learning was proposed to addressThe self-supervised representation learning uses a sequence-based Triplet Loss.A comprehensive comparison study was also conducted with various feature extractors and unary classifiersmodel can detect progressive faults very quickly and achieve improved results for comparison without feature

Keywords： fault detection     unary classification     self-supervised representation learning     multivariate nonlinear    

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Federated unsupervised representation learning Research Article

Fengda ZHANG, Kun KUANG, Long CHEN, Zhaoyang YOU, Tao SHEN, Jun XIAO, Yin ZHANG, Chao WU, Fei WU, Yueting ZHUANG, Xiaolin LI,fdzhang@zju.edu.cn,kunkuang@zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 8,   Pages 1181-1193 doi: 10.1631/FITEE.2200268

Abstract: amount of unlabeled data on distributed edge devices, we formulate a new problem in called federated unsupervised

Keywords： Federated learning     Unsupervised learning     Representation learning     Contrastive learning    

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Static-based early-damage detection using symbolic data analysis and unsupervised learning methods

João Pedro SANTOS,Christian CREMONA,André D. ORCESI,Paulo SILVEIRA,Luis CALADO

Frontiers of Structural and Civil Engineering 2015, Volume 9, Issue 1,   Pages 1-16 doi: 10.1007/s11709-014-0277-3

Abstract: large amount of researches and studies have been recently performed by applying statistical and machine learningdata driven strategy is proposed, consisting of the combination of advanced statistical and machine learning

Keywords： structural health monitoring     early-damage detection     principal component analysis     symbolic data     symbolic dissimilarity measures     cluster analysis     numerical model     damage simulations    

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Dynamic simulation of gas turbines via feature similarity-based transfer learning

Dengji ZHOU, Jiarui HAO, Dawen HUANG, Xingyun JIA, Huisheng ZHANG

Frontiers in Energy 2020, Volume 14, Issue 4,   Pages 817-835 doi: 10.1007/s11708-020-0709-9

Abstract: To tackle the issue, a novel transfer learning framework is proposed to transfer the knowledge from thedynamic operating data set with steep slope signals is created based on physics equations and then a featuresimilarity-based learning model with an encoder and a decoder is built and trained to achieve featureMoreover, compared with the other classical transfer learning modes, the method proposed has the bestthe hyper parameters indicates that the method proposed is able to adaptively balance the weight of learning

Keywords： gas turbine     dynamic simulation     data-driven     transfer learning     feature similarity    

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BUEES: a bottom-up event extraction system

Xiao DING,Bing QIN,Ting LIU

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 7,   Pages 541-552 doi: 10.1631/FITEE.1400405

Abstract: present BUEES, a bottom-up event extraction system, which extracts events from the web in a completely unsupervised

Keywords： Event extraction     Unsupervised learning     Bottom-up    

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Dynamic parameterized learning for unsupervised domain adaptation Research Article

Runhua JIANG, Yahong HAN

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 11,   Pages 1616-1632 doi: 10.1631/FITEE.2200631

Abstract: enables neural networks to transfer from a labeled source domain to an unlabeled target domain by learningMost of them, however, ignore exploration of the dynamic trade-off between and learning, thus renderingTo address these issues, we introduce the dynamic parameterized learning framework.semantic knowledge, the dynamic alignment parameter is proposed, to adaptively adjust the of and learning

Keywords： Unsupervised domain adaptation     Optimization steps     Domain alignment     Semantic discrimination    

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Two-level hierarchical feature learning for image classification Article

Guang-hui SONG,Xiao-gang JIN,Gen-lang CHEN,Yan NIE

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 9,   Pages 897-906 doi: 10.1631/FITEE.1500346

Abstract: In this paper, we propose a novel two-level hierarchical feature learning framework based on the deepFirst, the deep feature extractors of different levels are trained using the transfer learning methodSecond, the general feature extracted from all the categories and the specific feature extracted fromhighly similar categories are fused into a feature vector.learning is powerful.

Keywords： Transfer learning     Feature learning     Deep convolutional neural network     Hierarchical classification    

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Layer-wise domain correction for unsupervised domain adaptation Article

Shuang LI, Shi-ji SONG, Cheng WU

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 1,   Pages 91-103 doi: 10.1631/FITEE.1700774

Abstract: Deep neural networks have been successfully applied to numerous machine learning tasks because of theirimpressive feature abstraction capabilities.address the domain shift or data bias problems, we introduce layer-wise domain correction (LDC), a new unsupervised

Keywords： Unsupervised domain adaptation     Maximum mean discrepancy     Residual network     Deep learning    

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Feature selection techniques for microarray datasets: a comprehensive review, taxonomy, and future directions Review

Kulanthaivel BALAKRISHNAN, Ramasamy DHANALAKSHMI,bala.k.btech@gmail.com,r_dhanalakshmi@yahoo.com

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 10,   Pages 1451-1478 doi: 10.1631/FITEE.2100569

Abstract:

For optimal results, retrieving a relevant feature from a has become a hot topic for researchers involveds to work on multiclass classification problems and on different ways to enhance the performance of learningway for comprehending and highlighting the multitude of challenges and issues in finding the optimal featureaccuracy and convergence ability of several wrappers and hybrid algorithms to identify the optimal feature

Keywords： Feature selection     High dimensionality     Learning techniques     Microarray dataset    

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Iterative HOEO fusion strategy: a promising tool for enhancing bearing fault feature

Frontiers of Mechanical Engineering 2023, Volume 18, Issue 1, doi: 10.1007/s11465-022-0725-z

Abstract: energy operator (EO) and its variants have received considerable attention in the field of bearing fault featureaddress these issues, this paper presents a novel EO fusion strategy for enhancing the bearing fault featureSecond, an enhanced manifold learning algorithm is performed on the normalized MDIM to extract the intrinsicverifications confirm that the proposed strategy is more effective for enhancing the bearing fault feature

Keywords： higher order energy operator     fault diagnosis     manifold learning     rolling element bearing     information    

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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) techniqueIt can also sparsely concentrate the feature information into a few dominant subspace coefficients.

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

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A software defect prediction method with metric compensation based on feature selection and transferlearning Research Article

Jinfu CHEN, Xiaoli WANG, Saihua CAI, Jiaping XU, Jingyi CHEN, Haibo CHEN,caisaih@ujs.edu.cn

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 5,   Pages 715-731 doi: 10.1631/FITEE.2100468

Abstract: Cross-project software solves the problem of insufficient training data for traditional , and overcomes the challenge of applying models learned from multiple different source projects to target project. At the same time, two new problems emerge: (1) too many irrelevant and redundant features in the model training process will affect the training efficiency and thus decrease the prediction accuracy of the model; (2) the distribution of metric values will vary greatly from project to project due to the development environment and other factors, resulting in lower prediction accuracy when the model achieves cross-project prediction. In the proposed method, the Pearson method is introduced to address data redundancy, and the based technique is used to address the problem of large differences in data distribution between the source project and target project. In this paper, we propose a software method with based on and . The experimental results show that the model constructed with this method achieves better results on area under the receiver operating characteristic curve (AUC) value and F1-measure metric.

Keywords： Defect prediction     Feature selection     Transfer learning     Metric compensation    

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Title Author Date Type Operation

Speech emotion recognitionwith unsupervised feature learning

Zheng-wei HUANG,Wen-tao XUE,Qi-rong MAO

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

Imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph feature learning

Journal Article

Unknown fault detection for EGT multi-temperature signals based on self-supervised feature learning and

Journal Article

Federated unsupervised representation learning

Fengda ZHANG, Kun KUANG, Long CHEN, Zhaoyang YOU, Tao SHEN, Jun XIAO, Yin ZHANG, Chao WU, Fei WU, Yueting ZHUANG, Xiaolin LI,fdzhang@zju.edu.cn,kunkuang@zju.edu.cn

Journal Article

Static-based early-damage detection using symbolic data analysis and unsupervised learning methods

João Pedro SANTOS,Christian CREMONA,André D. ORCESI,Paulo SILVEIRA,Luis CALADO

Journal Article

Dynamic simulation of gas turbines via feature similarity-based transfer learning

Dengji ZHOU, Jiarui HAO, Dawen HUANG, Xingyun JIA, Huisheng ZHANG

Journal Article

BUEES: a bottom-up event extraction system

Xiao DING,Bing QIN,Ting LIU

Journal Article

Dynamic parameterized learning for unsupervised domain adaptation

Runhua JIANG, Yahong HAN

Journal Article

Two-level hierarchical feature learning for image classification

Guang-hui SONG,Xiao-gang JIN,Gen-lang CHEN,Yan NIE

Journal Article

Layer-wise domain correction for unsupervised domain adaptation

Shuang LI, Shi-ji SONG, Cheng WU

Journal Article

Feature selection techniques for microarray datasets: a comprehensive review, taxonomy, and future directions

Kulanthaivel BALAKRISHNAN, Ramasamy DHANALAKSHMI,bala.k.btech@gmail.com,r_dhanalakshmi@yahoo.com

Journal Article

Iterative HOEO fusion strategy: a promising tool for enhancing bearing fault feature

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

A software defect prediction method with metric compensation based on feature selection and transferlearning

Jinfu CHEN, Xiaoli WANG, Saihua CAI, Jiaping XU, Jingyi CHEN, Haibo CHEN,caisaih@ujs.edu.cn

Journal Article