Results 1 to 10 of about 33,680 (120)
Incomplete Multi-view Classification via Discriminative and Sparse Representation
Jisuanji kexue yu tansuo, 2021 Generally, the traditional multi-view learning methods assume that all samples are completed in all views. However, this assumption often fails in real applications because of limited access to data, equipment malfunc-tion, as well as occlusion and so on.
XIN Like, YANG Wanqi, YANG Ming
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Incomplete Multi-view Embedded Learning Method Based on Double Locality Preserving [PDF]
Jisuanji gongcheng, 2021 Most of the existing multi-view dimensionality reduction methods assume that the data is complete, but it is unrealistic for practical applications.In order to solve the problems in the dimensionality reduction of incomplete multi-view data, this paper ...
LIU Yanwen, ZHANG Jinxin, ZHANG Hongjie, JING Ling
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Effective Incomplete Multi-View Clustering via Low-Rank Graph Tensor Completion
Mathematics, 2023 In the past decade, multi-view clustering has received a lot of attention due to the popularity of multi-view data. However, not all samples can be observed from every view due to some unavoidable factors, resulting in the incomplete multi-view ...
Jinshi Yu +4 more
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IET Computer Vision, 2023 Incomplete Multi‐View Clustering aims to enhance clustering performance by using data from multiple modalities. Despite the fact that several approaches for studying this issue have been proposed, the following drawbacks still persist: (1) It is ...
Jiatai Wang +4 more
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Anchor Pseudo-Supervise Large-Scale Incomplete Multi-View Clustering
IEEE Access, 2023 In real life, only partial information of samples is available everywhere, this makes Incomplete multi-view clustering (IMVC) becomes a significant research topic to handle data loss situations.
Songbai Zhu +3 more
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Adaptive Weighted Graph Fusion Incomplete Multi-View Subspace Clustering
Sensors, 2020 With the enormous amount of multi-source data produced by various sensors and feature extraction approaches, multi-view clustering (MVC) has attracted developing research attention and is widely exploited in data analysis. Most of the existing multi-view
Pei Zhang +6 more
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Auto-Weighted Incomplete Multi-View Clustering
IEEE Access, 2020 Nowadays, multi-view clustering has attracted more and more attention, which provides a way to partition multi-view data into their corresponding clusters. Previous studies assume that each data instance appears in all views.
Wanyu Deng +3 more
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Incomplete Multi-View Clustering via Auto-Weighted Fusion in Partition Space
Tsinghua Science and Technology, 2023 As a class of effective methods for incomplete multi-view clustering, graph-based algorithms have recently drawn wide attention. However, most of them could use further improvement regarding the following aspects.
Dongxue Xia, Yan Yang, Shuhong Yang
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Multi-View Spectral Clustering With Incomplete Graphs
IEEE Access, 2020 Traditional multi-view learning usually assumes each instance appears in all views. However, in real-world applications, it is not an uncommon case that a number of instances suffer from some view samples missing.
Wenzhang Zhuge +4 more
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Remote Sensing, 2021 Recently, unstructured 3D point clouds have been widely used in remote sensing application. However, inevitable is the appearance of an incomplete point cloud, primarily due to the angle of view and blocking limitations. Therefore, point cloud completion
Weichao Wu +4 more
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