Literatura académica sobre el tema "Sparse Low-Rank Representation"
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Artículos de revistas sobre el tema "Sparse Low-Rank Representation"
Hengdong Zhu, Hengdong Zhu, Ting Yang Hengdong Zhu, Yingcang Ma Ting Yang y Xiaofei Yang Yingcang Ma. "Multi-view Re-weighted Sparse Subspace Clustering with Intact Low-rank Space Learning". 電腦學刊 33, n.º 4 (agosto de 2022): 121–31. http://dx.doi.org/10.53106/199115992022083304010.
Texto completoZhao, Jianxi y Lina Zhao. "Low-rank and sparse matrices fitting algorithm for low-rank representation". Computers & Mathematics with Applications 79, n.º 2 (enero de 2020): 407–25. http://dx.doi.org/10.1016/j.camwa.2019.07.012.
Texto completoKim, Hyuncheol y Joonki Paik. "Video Summarization using Low-Rank Sparse Representation". IEIE Transactions on Smart Processing & Computing 7, n.º 3 (30 de junio de 2018): 236–44. http://dx.doi.org/10.5573/ieiespc.2018.7.3.236.
Texto completoCHENG, Shilei, Song GU, Maoquan YE y Mei XIE. "Action Recognition Using Low-Rank Sparse Representation". IEICE Transactions on Information and Systems E101.D, n.º 3 (2018): 830–34. http://dx.doi.org/10.1587/transinf.2017edl8176.
Texto completoWang, Jun, Daming Shi, Dansong Cheng, Yongqiang Zhang y Junbin Gao. "LRSR: Low-Rank-Sparse representation for subspace clustering". Neurocomputing 214 (noviembre de 2016): 1026–37. http://dx.doi.org/10.1016/j.neucom.2016.07.015.
Texto completoDu, Haishun, Xudong Zhang, Qingpu Hu y Yandong Hou. "Sparse representation-based robust face recognition by graph regularized low-rank sparse representation recovery". Neurocomputing 164 (septiembre de 2015): 220–29. http://dx.doi.org/10.1016/j.neucom.2015.02.067.
Texto completoZhang, Xiujun, Chen Xu, Min Li y Xiaoli Sun. "Sparse and Low-Rank Coupling Image Segmentation Model Via Nonconvex Regularization". International Journal of Pattern Recognition and Artificial Intelligence 29, n.º 02 (27 de febrero de 2015): 1555004. http://dx.doi.org/10.1142/s0218001415550046.
Texto completoZheng, Chun-Hou, Yi-Fu Hou y Jun Zhang. "Improved sparse representation with low-rank representation for robust face recognition". Neurocomputing 198 (julio de 2016): 114–24. http://dx.doi.org/10.1016/j.neucom.2015.07.146.
Texto completoDu, Shiqiang, Yuqing Shi, Guangrong Shan, Weilan Wang y Yide Ma. "Tensor low-rank sparse representation for tensor subspace learning". Neurocomputing 440 (junio de 2021): 351–64. http://dx.doi.org/10.1016/j.neucom.2021.02.002.
Texto completoZou, Dongqing, Xiaowu Chen, Guangying Cao y Xiaogang Wang. "Unsupervised Video Matting via Sparse and Low-Rank Representation". IEEE Transactions on Pattern Analysis and Machine Intelligence 42, n.º 6 (1 de junio de 2020): 1501–14. http://dx.doi.org/10.1109/tpami.2019.2895331.
Texto completoTesis sobre el tema "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.
Texto completoThis 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.
Texto completoPh.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.
Texto completoHrbáček, 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.
Texto completoKolbábková, 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.
Texto completoLiu, 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.
Texto completoIncomplete 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.
Texto completoWeisbecker, 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.
Texto completoTeng, Luyao. "Research on Joint Sparse Representation Learning Approaches". Thesis, 2019. https://vuir.vu.edu.au/40024/.
Texto completoLibros sobre el tema "Sparse Low-Rank Representation"
Dai, Qionghai y Tsinghua University Tsinghua University Press. Multidimensional Signal Processing: Fast Transform, Sparse Representation, Low Rank Analysis. de Gruyter GmbH, Walter, 2027.
Buscar texto completoDai, Qionghai y Tsinghua University Tsinghua University Press. Multidimensional Signal Processing: Fast Transform, Sparse Representation, Low Rank Analysis. de Gruyter GmbH, Walter, 2027.
Buscar texto completoDai, Qionghai y Tsinghua University Tsinghua University Press. Multidimensional Signal Processing: Fast Transform, Sparse Representation, Low Rank Analysis. de Gruyter GmbH, Walter, 2027.
Buscar texto completoCapítulos de libros sobre el tema "Sparse Low-Rank Representation"
Liu, Guangcan y Shuicheng Yan. "Latent Low-Rank Representation". En 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.
Texto completoLiu, Guangcan y Shuicheng Yan. "Scalable Low-Rank Representation". En 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.
Texto completoLi, Jingshan, Caikou Chen, Xielian Hou y Rong Wang. "Laplacian Regularized Non-negative Sparse Low-Rank Representation Classification". En Biometric Recognition, 683–90. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69923-3_73.
Texto completoKang, Peipei, Xiaozhao Fang, Wei Zhang, Shaohua Teng, Lunke Fei, Yong Xu y Yubao Zheng. "Supervised Group Sparse Representation via Intra-class Low-Rank Constraint". En Biometric Recognition, 206–13. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-97909-0_22.
Texto completoXiao, Shijie, Mingkui Tan y Dong Xu. "Weighted Block-Sparse Low Rank Representation for Face Clustering in Videos". En Computer Vision – ECCV 2014, 123–38. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10599-4_9.
Texto completoWang, Ziqiang, Yingzhi Ouyang, Weidan Zhu, Bin Sun y Qiang Liu. "Common Subspace Based Low-Rank and Joint Sparse Representation for Multi-view Face Recognition". En Lecture Notes in Computer Science, 145–56. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34113-8_13.
Texto completoWang, Zhen-Chang, Jin-Xing Liu, Jun-Liang Shang, Ling-Yun Dai, Chun-Hou Zheng y Juan Wang. "ARGLRR: An Adjusted Random Walk Graph Regularization Sparse Low-Rank Representation Method for Single-Cell RNA-Sequencing Data Clustering". En Bioinformatics Research and Applications, 126–37. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-23198-8_12.
Texto completoDantas, Cássio F., Jérémy E. Cohen y Rémi Gribonval. "Learning Fast Dictionaries for Sparse Representations Using Low-Rank Tensor Decompositions". En 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.
Texto completoWang, Yitang, Tau Fu, Tianci Zhang y Xueguan Song. "A Denoising Method by Low-Rank and Sparse Representations and Its Application in Tunnel Boring Machine". En Lecture Notes in Electrical Engineering, 565–72. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3171-0_46.
Texto completoActas de conferencias sobre el tema "Sparse Low-Rank Representation"
Dao, Minh, Yuanming Suo, Sang Peter Chin y Trac D. Tran. "Structured sparse representation with low-rank interference". En 2014 48th Asilomar Conference on Signals, Systems and Computers. IEEE, 2014. http://dx.doi.org/10.1109/acssc.2014.7094407.
Texto completoLi, Jingshan, Caikou Chen, Xielian Hou, Tianchen Dai y Rong Wang. "Weighted non-negative sparse low-rank representation classification". En 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). IEEE, 2017. http://dx.doi.org/10.1109/iaeac.2017.8054398.
Texto completoZhang, Yongqiang, Daming Shi, Junbin Gao y Dansong Cheng. "Low-Rank-Sparse Subspace Representation for Robust Regression". En 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2017. http://dx.doi.org/10.1109/cvpr.2017.317.
Texto completoZou, Dongqing, Xiaowu Chen, Guangying Cao y Xiaogang Wang. "Video Matting via Sparse and Low-Rank Representation". En 2015 IEEE International Conference on Computer Vision (ICCV). IEEE, 2015. http://dx.doi.org/10.1109/iccv.2015.183.
Texto completoWang, Boyue, Yongli Hu, Junbin Gao, Yanfeng Sun y Baocai Yin. "Cascaded Low Rank and Sparse Representation on Grassmann Manifolds". En 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.
Texto completoLi, Jianwei, Xiaowu Chen, Dongqing Zou, Bo Gao y Wei Teng. "Conformal and Low-Rank Sparse Representation for Image Restoration". En 2015 IEEE International Conference on Computer Vision (ICCV). IEEE, 2015. http://dx.doi.org/10.1109/iccv.2015.35.
Texto completoHuang, Libo, Bingo Wing-Kuen Ling, Yan Zeng y Lu Gan. "Spike Sorting Based On Low-Rank And Sparse Representation". En 2020 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2020. http://dx.doi.org/10.1109/icme46284.2020.9102837.
Texto completoSun, Jing, Zongze Wu, Deyu Zeng y Zhigang Ren. "A New Representation for Data: Sparse and Low-Rank". En 2018 Chinese Automation Congress (CAC). IEEE, 2018. http://dx.doi.org/10.1109/cac.2018.8623248.
Texto completoLi, Tao, Weiwei Wang, Long Xu y Xiangchu Feng. "Image Denoising Using Low-Rank Dictionary and Sparse Representation". En 2014 Tenth International Conference on Computational Intelligence and Security (CIS). IEEE, 2014. http://dx.doi.org/10.1109/cis.2014.56.
Texto completoZhu, Yanping, Aimin Jiang, Xiaofeng Liu y Hon Keung Kwan. "Sparse representation and low-rank approximation for sensor signal processing". En 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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