Littérature scientifique sur le sujet « Sparse Low-Rank Representation »
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Articles de revues sur le sujet "Sparse Low-Rank Representation"
Hengdong Zhu, Hengdong Zhu, Ting Yang Hengdong Zhu, Yingcang Ma Ting Yang et Xiaofei Yang Yingcang Ma. « Multi-view Re-weighted Sparse Subspace Clustering with Intact Low-rank Space Learning ». 電腦學刊 33, no 4 (août 2022) : 121–31. http://dx.doi.org/10.53106/199115992022083304010.
Texte intégralZhao, Jianxi, et Lina Zhao. « Low-rank and sparse matrices fitting algorithm for low-rank representation ». Computers & ; Mathematics with Applications 79, no 2 (janvier 2020) : 407–25. http://dx.doi.org/10.1016/j.camwa.2019.07.012.
Texte intégralKim, Hyuncheol, et Joonki Paik. « Video Summarization using Low-Rank Sparse Representation ». IEIE Transactions on Smart Processing & ; Computing 7, no 3 (30 juin 2018) : 236–44. http://dx.doi.org/10.5573/ieiespc.2018.7.3.236.
Texte intégralCHENG, Shilei, Song GU, Maoquan YE et Mei XIE. « Action Recognition Using Low-Rank Sparse Representation ». IEICE Transactions on Information and Systems E101.D, no 3 (2018) : 830–34. http://dx.doi.org/10.1587/transinf.2017edl8176.
Texte intégralWang, Jun, Daming Shi, Dansong Cheng, Yongqiang Zhang et Junbin Gao. « LRSR : Low-Rank-Sparse representation for subspace clustering ». Neurocomputing 214 (novembre 2016) : 1026–37. http://dx.doi.org/10.1016/j.neucom.2016.07.015.
Texte intégralDu, Haishun, Xudong Zhang, Qingpu Hu et Yandong Hou. « Sparse representation-based robust face recognition by graph regularized low-rank sparse representation recovery ». Neurocomputing 164 (septembre 2015) : 220–29. http://dx.doi.org/10.1016/j.neucom.2015.02.067.
Texte intégralZhang, Xiujun, Chen Xu, Min Li et Xiaoli Sun. « Sparse and Low-Rank Coupling Image Segmentation Model Via Nonconvex Regularization ». International Journal of Pattern Recognition and Artificial Intelligence 29, no 02 (27 février 2015) : 1555004. http://dx.doi.org/10.1142/s0218001415550046.
Texte intégralZheng, Chun-Hou, Yi-Fu Hou et Jun Zhang. « Improved sparse representation with low-rank representation for robust face recognition ». Neurocomputing 198 (juillet 2016) : 114–24. http://dx.doi.org/10.1016/j.neucom.2015.07.146.
Texte intégralDu, Shiqiang, Yuqing Shi, Guangrong Shan, Weilan Wang et Yide Ma. « Tensor low-rank sparse representation for tensor subspace learning ». Neurocomputing 440 (juin 2021) : 351–64. http://dx.doi.org/10.1016/j.neucom.2021.02.002.
Texte intégralZou, Dongqing, Xiaowu Chen, Guangying Cao et Xiaogang Wang. « Unsupervised Video Matting via Sparse and Low-Rank Representation ». IEEE Transactions on Pattern Analysis and Machine Intelligence 42, no 6 (1 juin 2020) : 1501–14. http://dx.doi.org/10.1109/tpami.2019.2895331.
Texte intégralThèses sur le sujet "Sparse Low-Rank Representation"
Cordolino, Sobral Andrews. « Robust low-rank and sparse decomposition for moving object detection : from matrices to tensors ». Thesis, La Rochelle, 2017. http://www.theses.fr/2017LAROS007/document.
Texte intégralThis thesis introduces the recent advances on decomposition into low-rank plus sparse matrices and tensors, as well as the main contributions to face the principal issues in moving object detection. First, we present an overview of the state-of-the-art methods for low-rank and sparse decomposition, as well as their application to background modeling and foreground segmentation tasks. Next, we address the problem of background model initialization as a reconstruction process from missing/corrupted data. A novel methodology is presented showing an attractive potential for background modeling initialization in video surveillance. Subsequently, we propose a double-constrained version of robust principal component analysis to improve the foreground detection in maritime environments for automated video-surveillance applications. The algorithm makes use of double constraints extracted from spatial saliency maps to enhance object foreground detection in dynamic scenes. We also developed two incremental tensor-based algorithms in order to perform background/foreground separation from multidimensional streaming data. These works address the problem of low-rank and sparse decomposition on tensors. Finally, we present a particular work realized in conjunction with the Computer Vision Center (CVC) at Autonomous University of Barcelona (UAB)
Oreifej, Omar. « Robust Subspace Estimation Using Low-Rank Optimization. Theory and Applications in Scene Reconstruction, Video Denoising, and Activity Recognition ». Doctoral diss., University of Central Florida, 2013. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/5684.
Texte intégralPh.D.
Doctorate
Electrical Engineering and Computing
Engineering and Computer Science
Computer Engineering
Zhang, Yu. « Investigating Key Techniques to Leverage the Functionality of Ground/Wall Penetrating Radar ». ScholarWorks @ UVM, 2017. http://scholarworks.uvm.edu/graddis/799.
Texte intégralHrbáček, Radek. « Využití řídké reprezentace signálu při snímání a rekonstrukci v nukleární magnetické rezonanci ». Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2013. http://www.nusl.cz/ntk/nusl-220303.
Texte intégralKolbábková, Anežka. « Algoritmy doplňování chybějících dat v audiosignálech ». Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2014. http://www.nusl.cz/ntk/nusl-231131.
Texte intégralLiu, Zhenjiao. « Incomplete multi-view data clustering with hidden data mining and fusion techniques ». Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAS011.
Texte intégralIncomplete multi-view data clustering is a research direction that attracts attention in the fields of data mining and machine learning. In practical applications, we often face situations where only part of the modal data can be obtained or there are missing values. Data fusion is an important method for incomplete multi-view information mining. Solving incomplete multi-view information mining in a targeted manner, achieving flexible collaboration between visible views and shared hidden views, and improving the robustness have become quite challenging. This thesis focuses on three aspects: hidden data mining, collaborative fusion, and enhancing the robustness of clustering. The main contributions are as follows:1. Hidden data mining for incomplete multi-view data: existing algorithms cannot make full use of the observation of information within and between views, resulting in the loss of a large amount of valuable information, and so we propose a new incomplete multi-view clustering model IMC-NLT (Incomplete Multi-view Clustering Based on NMF and Low-Rank Tensor Fusion) based on non-negative matrix factorization and low-rank tensor fusion. IMC-NLT first uses a low-rank tensor to retain view features with a unified dimension. Using a consistency measure, IMC-NLT captures a consistent representation across multiple views. Finally, IMC-NLT incorporates multiple learning into a unified model such that hidden information can be extracted effectively from incomplete views. We conducted comprehensive experiments on five real-world datasets to validate the performance of IMC-NLT. The overall experimental results demonstrate that the proposed IMC-NLT performs better than several baseline methods, yielding stable and promising results.2. Collaborative fusion for incomplete multi-view data: our approach to address this issue is Incomplete Multi-view Co-Clustering by Sparse Low-Rank Representation (CCIM-SLR). The algorithm is based on sparse low-rank representation and subspace representation, in which jointly missing data is filled using data within a modality and related data from other modalities. To improve the stability of clustering results for multi-view data with different missing degrees, CCIM-SLR uses the Γ-norm model, which is an adjustable low-rank representation method. CCIM-SLR can alternate between learning the shared hidden view, visible view, and cluster partitions within a co-learning framework. An iterative algorithm with guaranteed convergence is used to optimize the proposed objective function. Compared with other baseline models, CCIM-SLR achieved the best performance in the comprehensive experiments on the five benchmark datasets, particularly on those with varying degrees of incompleteness.3. Enhancing the clustering robustness for incomplete multi-view data: we offer a fusion of graph convolution and information bottlenecks (Incomplete Multi-view Representation Learning Through Anchor Graph-based GCN and Information Bottleneck - IMRL-AGI). First, we introduce the information bottleneck theory to filter out the noise data with irrelevant details and retain only the most relevant feature items. Next, we integrate the graph structure information based on anchor points into the local graph information of the state fused into the shared information representation and the information representation learning process of the local specific view, a process that can balance the robustness of the learned features and improve the robustness. Finally, the model integrates multiple representations with the help of information bottlenecks, reducing the impact of redundant information in the data. Extensive experiments are conducted on several real-world datasets, and the results demonstrate the superiority of IMRL-AGI. Specifically, IMRL-AGI shows significant improvements in clustering and classification accuracy, even in the presence of high view missing rates (e.g. 10.23% and 24.1% respectively on the ORL dataset)
Mangová, Marie. « Komprimované snímání v perfuzním zobrazování pomocí magnetické rezonance ». Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2014. http://www.nusl.cz/ntk/nusl-231150.
Texte intégralWeisbecker, Clément. « Improving multifrontal solvers by means of algebraic Block Low-Rank representations ». Phd thesis, Toulouse, INPT, 2013. http://oatao.univ-toulouse.fr/10506/1/weisbecker.pdf.
Texte intégralTeng, Luyao. « Research on Joint Sparse Representation Learning Approaches ». Thesis, 2019. https://vuir.vu.edu.au/40024/.
Texte intégralLivres sur le sujet "Sparse Low-Rank Representation"
Dai, Qionghai, et Tsinghua University Tsinghua University Press. Multidimensional Signal Processing : Fast Transform, Sparse Representation, Low Rank Analysis. de Gruyter GmbH, Walter, 2027.
Trouver le texte intégralDai, Qionghai, et Tsinghua University Tsinghua University Press. Multidimensional Signal Processing : Fast Transform, Sparse Representation, Low Rank Analysis. de Gruyter GmbH, Walter, 2027.
Trouver le texte intégralDai, Qionghai, et Tsinghua University Tsinghua University Press. Multidimensional Signal Processing : Fast Transform, Sparse Representation, Low Rank Analysis. de Gruyter GmbH, Walter, 2027.
Trouver le texte intégralChapitres de livres sur le sujet "Sparse Low-Rank Representation"
Liu, Guangcan, et Shuicheng Yan. « Latent Low-Rank Representation ». Dans Low-Rank and Sparse Modeling for Visual Analysis, 23–38. Cham : Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12000-3_2.
Texte intégralLiu, Guangcan, et Shuicheng Yan. « Scalable Low-Rank Representation ». Dans Low-Rank and Sparse Modeling for Visual Analysis, 39–60. Cham : Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12000-3_3.
Texte intégralLi, Jingshan, Caikou Chen, Xielian Hou et Rong Wang. « Laplacian Regularized Non-negative Sparse Low-Rank Representation Classification ». Dans Biometric Recognition, 683–90. Cham : Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69923-3_73.
Texte intégralKang, Peipei, Xiaozhao Fang, Wei Zhang, Shaohua Teng, Lunke Fei, Yong Xu et Yubao Zheng. « Supervised Group Sparse Representation via Intra-class Low-Rank Constraint ». Dans Biometric Recognition, 206–13. Cham : Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-97909-0_22.
Texte intégralXiao, Shijie, Mingkui Tan et Dong Xu. « Weighted Block-Sparse Low Rank Representation for Face Clustering in Videos ». Dans Computer Vision – ECCV 2014, 123–38. Cham : Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10599-4_9.
Texte intégralWang, Ziqiang, Yingzhi Ouyang, Weidan Zhu, Bin Sun et Qiang Liu. « Common Subspace Based Low-Rank and Joint Sparse Representation for Multi-view Face Recognition ». Dans Lecture Notes in Computer Science, 145–56. Cham : Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34113-8_13.
Texte intégralWang, Zhen-Chang, Jin-Xing Liu, Jun-Liang Shang, Ling-Yun Dai, Chun-Hou Zheng et Juan Wang. « ARGLRR : An Adjusted Random Walk Graph Regularization Sparse Low-Rank Representation Method for Single-Cell RNA-Sequencing Data Clustering ». Dans Bioinformatics Research and Applications, 126–37. Cham : Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-23198-8_12.
Texte intégralDantas, Cássio F., Jérémy E. Cohen et Rémi Gribonval. « Learning Fast Dictionaries for Sparse Representations Using Low-Rank Tensor Decompositions ». Dans Latent Variable Analysis and Signal Separation, 456–66. Cham : Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93764-9_42.
Texte intégralWang, Yitang, Tau Fu, Tianci Zhang et Xueguan Song. « A Denoising Method by Low-Rank and Sparse Representations and Its Application in Tunnel Boring Machine ». Dans Lecture Notes in Electrical Engineering, 565–72. Singapore : Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3171-0_46.
Texte intégralActes de conférences sur le sujet "Sparse Low-Rank Representation"
Dao, Minh, Yuanming Suo, Sang Peter Chin et Trac D. Tran. « Structured sparse representation with low-rank interference ». Dans 2014 48th Asilomar Conference on Signals, Systems and Computers. IEEE, 2014. http://dx.doi.org/10.1109/acssc.2014.7094407.
Texte intégralLi, Jingshan, Caikou Chen, Xielian Hou, Tianchen Dai et Rong Wang. « Weighted non-negative sparse low-rank representation classification ». Dans 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). IEEE, 2017. http://dx.doi.org/10.1109/iaeac.2017.8054398.
Texte intégralZhang, Yongqiang, Daming Shi, Junbin Gao et Dansong Cheng. « Low-Rank-Sparse Subspace Representation for Robust Regression ». Dans 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2017. http://dx.doi.org/10.1109/cvpr.2017.317.
Texte intégralZou, Dongqing, Xiaowu Chen, Guangying Cao et Xiaogang Wang. « Video Matting via Sparse and Low-Rank Representation ». Dans 2015 IEEE International Conference on Computer Vision (ICCV). IEEE, 2015. http://dx.doi.org/10.1109/iccv.2015.183.
Texte intégralWang, Boyue, Yongli Hu, Junbin Gao, Yanfeng Sun et Baocai Yin. « Cascaded Low Rank and Sparse Representation on Grassmann Manifolds ». Dans Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California : International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/382.
Texte intégralLi, Jianwei, Xiaowu Chen, Dongqing Zou, Bo Gao et Wei Teng. « Conformal and Low-Rank Sparse Representation for Image Restoration ». Dans 2015 IEEE International Conference on Computer Vision (ICCV). IEEE, 2015. http://dx.doi.org/10.1109/iccv.2015.35.
Texte intégralHuang, Libo, Bingo Wing-Kuen Ling, Yan Zeng et Lu Gan. « Spike Sorting Based On Low-Rank And Sparse Representation ». Dans 2020 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2020. http://dx.doi.org/10.1109/icme46284.2020.9102837.
Texte intégralSun, Jing, Zongze Wu, Deyu Zeng et Zhigang Ren. « A New Representation for Data : Sparse and Low-Rank ». Dans 2018 Chinese Automation Congress (CAC). IEEE, 2018. http://dx.doi.org/10.1109/cac.2018.8623248.
Texte intégralLi, Tao, Weiwei Wang, Long Xu et Xiangchu Feng. « Image Denoising Using Low-Rank Dictionary and Sparse Representation ». Dans 2014 Tenth International Conference on Computational Intelligence and Security (CIS). IEEE, 2014. http://dx.doi.org/10.1109/cis.2014.56.
Texte intégralZhu, Yanping, Aimin Jiang, Xiaofeng Liu et Hon Keung Kwan. « Sparse representation and low-rank approximation for sensor signal processing ». Dans 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE). IEEE, 2017. http://dx.doi.org/10.1109/ccece.2017.7946701.
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