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作品数：256被引量：361H指数：7
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A novel method for clustering cellular data to improve classification
《Neural Regeneration Research》2025年第9期2697-2705,共9页Diek W.Wheeler Giorgio A.Ascoli 
supported in part by NIH grants R01NS39600,U01MH114829;RF1MH128693(to GAA)。
Many fields,such as neuroscience,are experiencing the vast prolife ration of cellular data,underscoring the need fo r organizing and interpreting large datasets.A popular approach partitions data into manageable subse...
关键词：cellular data clustering dendrogram data classification Levene's one-tailed statistical test unsupervised hierarchical clustering 
Robust domain adaptation with noisy and shifted label distribution
《Frontiers of Computer Science》2025年第3期25-36,共12页Shao-Yuan LI Shi-Ji ZHAO Zheng-Tao CAO Sheng-Jun HUANG Songcan CHEN 
supported by the National Key R&D Program of China(2022ZD0114801);the National Natural Science Foundation of China(Grant No.61906089);the Jiangsu Province Basic Research Program(BK20190408).
Unsupervised Domain Adaptation(UDA)intends to achieve excellent results by transferring knowledge from labeled source domains to unlabeled target domains in which the data or label distribution changes.Previous UDA me...
关键词：unsupervised domain adaptation label noise label distribution shift SELF-TRAINING class rebalancing 
Two-Stream Auto-Encoder Network for Unsupervised Skeleton-Based Action Recognition
《Journal of Shanghai Jiaotong university(Science)》2025年第2期330-336,共7页WANG Gang GUAN Yaonan LI Dewei 
Representation learning from unlabeled skeleton data is a challenging task.Prior unsupervised learning algorithms mainly rely on the modeling ability of recurrent neural networks to extract the action representations....
关键词：representation learning skeleton-based action recognition unsupervised deep learning 
Nonconvex and discriminative transfer subspace learning for unsupervised domain adaptation
《Frontiers of Computer Science》2025年第2期43-57,共15页Yueying LIU Tingjin LUO 
supported by the National Natural Science Foundation of China(Grant No.61922087);the Huxiang Young Talents Program of Hunan Province(2021RC3070).
Unsupervised transfer subspace learning is one of the challenging and important topics in domain adaptation,which aims to classify unlabeled target data by using source domain information.The traditional transfer subs...
关键词：transfer subspace learning unsupervised domain adaptation low-rank modeling nonconvex optimization 
Unsupervised vehicle re-identification via meta-type generalization
《High Technology Letters》2025年第1期32-40,共9页HUANG Chengti ZHANG Xiaoxiang ZHAO Qianqian ZHU Jianqing 
Supported by the National Natural Science Foundation of China(No.61976098);the Natural Science Foundation for Outstanding Young Scholars of Fujian Province(No.2022J06023).
Unsupervised vehicle re-identification(Re-ID)methods have garnered widespread attention due to their potential in real-world traffic monitoring.However,existing unsupervised domain adaptation techniques often rely on ...
关键词：deep learning unsupervised vehicle re-identification(Re-ID) META-LEARNING 
Pseudo Label Purification with Dual Contrastive Learning for Unsupervised Vehicle Re-Identification
《Computers, Materials & Continua》2025年第3期3921-3941,共21页Jiyang Xu Qi Wang Xin Xiong Weidong Min Jiang Luo Di Gai Qing Han 
supported by the National Natural Science Foundation of China under Grant Nos.62461037,62076117 and 62166026;the Jiangxi Provincial Natural Science Foundation under Grant Nos.20224BAB212011,20232BAB202051,20232BAB212008 and 20242BAB25078;the Jiangxi Provincial Key Laboratory of Virtual Reality under Grant No.2024SSY03151.
The unsupervised vehicle re-identification task aims at identifying specific vehicles in surveillance videos without utilizing annotation information.Due to the higher similarity in appearance between vehicles compare...
关键词：Unsupervised vehicle re-identification dual contrastive learning pseudo label refinement knowledge distillation 
Unsupervised multiplex graph diffusion networks with multi-level canonical correlation analysis for multiplex graph representation learning
《Science China(Information Sciences)》2025年第3期62-75,共14页Sichao FU Qinmu PENG Yange HE Baokun DU Bin ZOU Xiao-Yuan JING Xinge YOU 
supported in part by National Key Research and Development Program of China(Grant No.2022YFF0712300);National Natural Science Foundation of China(Grant No.62172177);Knowledge Innovation Program of Wuhan-Shuguang;Fundamental Research Funds for the Central Universities(HUST)(Grant No.2022JYCXJJ034);Open Research Fund from Shandong Provincial Key Laboratory of Computer Network(Grant No.SKLCN-2021-02)。
In recent years,unsupervised multiplex graph representation learning(UMGRL)has received increasing research interest,which aims to learn discriminative node features from the multiplex graphs supervised by data withou...
关键词：unsupervised multiplex graph representation learning graph neural networks node classification node clustering 
Unsupervised Low-Light Image Enhancement Based on Explicit Denoising and Knowledge Distillation
《Computers, Materials & Continua》2025年第2期2537-2554,共18页Wenkai Zhang Hao Zhang Xianming Liu Xiaoyu Guo Xinzhe Wang Shuiwang Li 
support by the Guangxi Natural Science Foundation(Grant No.2024GXNSFAA010484);the NationalNatural Science Foundation of China(No.62466013),this work has been made possible.
Under low-illumination conditions, the quality of image signals deteriorates significantly, typically characterized by a peak signal-to-noise ratio (PSNR) below 10 dB, which severely limits the usability of the images...
关键词：Deep learning low-light image enhancement real-time processing knowledge distillation 
Unsupervised Meteorological Downscaling Based on Dual Learning and Subgrid-scale Auxiliary Information
《Advances in Atmospheric Sciences》2025年第1期53-66,共14页Jing HU Jialing MU Xiaomeng HUANG Xi WU 
supported by the following funding bodies:the National Key Research and Development Program of China(Grant No.2020YFA0608000);National Science Foundation of China(Grant Nos.42075142,42375148,42125503;42130608);FY-APP-2022.0609,Sichuan Province Key Tech nology Research and Development project(Grant Nos.2024ZHCG0168,2024ZHCG0176,2023YFG0305,2023YFG-0124,and 23ZDYF0091);the CUIT Science and Technology Innovation Capacity Enhancement Program project(Grant No.KYQN202305)。
Climate downscaling is used to transform large-scale meteorological data into small-scale data with enhanced detail,which finds wide applications in climate modeling,numerical weather forecasting,and renewable energy....
关键词：DOWNSCALING UNSUPERVISED deep learning dual learning auxiliary information 
Unsupervised machine learning approach for tailoring educational content to individual student weaknesses
《High-Confidence Computing》2024年第4期91-104,共14页Shabab Intishar Rahman Shadman Ahmed Tasnim Akter Fariha Ammar Mohammad Muhammad Nayeem Mubasshirul Haque Sriram Chellappan Jannatun Noor 
By analyzing data gathered through Online Learning(OL)systems,data mining can be used to unearth hidden relationships between topics and trends in student performance.Here,in this paper,we show how data mining techniq...
关键词：Recommendation system Clustering F-P growth APRIORI Associative pattern E-learning sphere Online learning 
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