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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

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

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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 learning domain-invariant representations. Recent approaches achieve this by directly matching the marginal distributions of these two domains. Most of them, however, ignore exploration of the dynamic trade-off between and learning, thus rendering them susceptible to the problems of negative transfer and outlier samples. To address these issues, we introduce the dynamic parameterized learning framework. First, by exploring domain-level semantic knowledge, the dynamic alignment parameter is proposed, to adaptively adjust the of and learning. Besides, for obtaining semantic-discriminative and domain-invariant representations, we propose to align training trajectories on both source and target domains. Comprehensive experiments are conducted to validate the effectiveness of the proposed methods, and extensive comparisons are conducted on seven datasets of three visual tasks to demonstrate their practicability.

Keywords： Unsupervised domain adaptation     Optimization steps     Domain alignment     Semantic discrimination    

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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: Because 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 learning

Keywords： Unsupervised     Local learning     Group sparse regression     Feature selection    

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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: 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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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: A large amount of researches and studies have been recently performed by applying statistical and machine learning techniques for vibration-based damage detection. However, the global character inherent to the limited number of modal properties issued from operational modal analysis may be not appropriate for early-damage, which has generally a local character. The present paper aims at detecting this type of damage by using static SHM data and by assuming that early-damage produces dead load redistribution. To achieve this objective a data driven strategy is proposed, consisting of the combination of advanced statistical and machine learning methods such as principal component analysis, symbolic data analysis and cluster analysis. From this analysis it was observed that, under the noise levels measured on site, the proposed strategy is able to automatically detect stiffness reduction in stay cables reaching at least 1%.

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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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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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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Unsupervised object detection with scene-adaptive concept learning Research Articles

Shiliang Pu, Wei Zhao, Weijie Chen, Shicai Yang, Di Xie, Yunhe Pan,xiedi@hikvision.com

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 5,   Pages 615-766 doi: 10.1631/FITEE.2000567

Abstract: Object detection is one of the hottest research directions in computer vision, has already made impressive progress in academia, and has many valuable applications in the industry. However, the mainstream detection methods still have two shortcomings: (1) even a model that is well trained using large amounts of data still cannot generally be used across different kinds of scenes; (2) once a model is deployed, it cannot autonomously evolve along with the accumulated unlabeled scene data. To address these problems, and inspired by theory, we propose a novel scene-adaptive evolution algorithm that can decrease the impact of scene changes through the concept of object groups. We first extract a large number of object proposals from unlabeled data through a pre-trained detection model. Second, we build the dictionary of object concepts by clustering the proposals, in which each cluster center represents an object prototype. Third, we look into the relations between different clusters and the object information of different groups, and propose a graph-based group information propagation strategy to determine the category of an object concept, which can effectively distinguish positive and negative proposals. With these pseudo labels, we can easily fine-tune the pre-trained model. The effectiveness of the proposed method is verified by performing different experiments, and the significant improvements are achieved.

Keywords： 视觉知识；无监督视频目标检测；场景自适应学习    

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FAAD: an unsupervised fast and accurate anomaly detectionmethod for amulti-dimensional sequence over Regular Papers

Bin LI, Yi-jie WANG, Dong-sheng YANG, Yong-mou LI, Xing-kong MA

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 3,   Pages 388-404 doi: 10.1631/FITEE.1800038

Abstract: performance of anomaly detection for a multi-dimensional sequence over the data stream, we propose a novel unsupervised

Keywords： Data stream     Multi-dimensional sequence     Anomaly detection     Concept drift     Feature selection    

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Neuro-heuristic computational intelligence for solving nonlinear pantograph systems Article

Muhammad Asif Zahoor RAJA, Iftikhar AHMAD, Imtiaz KHAN, Muhammed Ibrahem SYAM, Abdul Majid WAZWAZ

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 4,   Pages 464-484 doi: 10.1631/FITEE.1500393

Abstract: Two types of mathematical models of the systems are constructed with the help of ANNs by defining an unsupervised

Keywords： Neural networks     Initial value problems (IVPs)     Functional differential equations (FDEs)     Unsupervised    

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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

Dynamic parameterized learning for unsupervised domain adaptation

Runhua JIANG, Yahong HAN

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

Layer-wise domain correction for unsupervised domain adaptation

Shuang LI, Shi-ji SONG, Cheng WU

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

BUEES: a bottom-up event extraction system

Xiao DING,Bing QIN,Ting LIU

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

Unsupervised object detection with scene-adaptive concept learning

Shiliang Pu, Wei Zhao, Weijie Chen, Shicai Yang, Di Xie, Yunhe Pan,xiedi@hikvision.com

Journal Article

FAAD: an unsupervised fast and accurate anomaly detectionmethod for amulti-dimensional sequence over

Bin LI, Yi-jie WANG, Dong-sheng YANG, Yong-mou LI, Xing-kong MA

Journal Article

Neuro-heuristic computational intelligence for solving nonlinear pantograph systems

Muhammad Asif Zahoor RAJA, Iftikhar AHMAD, Imtiaz KHAN, Muhammed Ibrahem SYAM, Abdul Majid WAZWAZ

Journal Article

Shi Haoyue: Supervisory Signals in Unsupervised Parsing Model (2020-11-6)

14 Oct 2022

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