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Incomplete multi-view learning: Review, analysis, and prospects
Applied Soft Computing JournalYingjie Tian, Jingjing Tang, Saiji Fu
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Incomplete multi-view learning via half-quadratic minimization
Neurocomputing, 2021Abstract In real applications, to deal with incomplete multi-view data, incomplete multi-view learning has experienced rapid development in recent years. Among various incomplete multi-view learning methods, a considerable number of methods were developed with the matrix factorization technique. Most of the existing matrix factorization based methods
Jiacheng Jiang +4 more
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Incomplete Multi-view Clustering via Subspace Learning
Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, 2015Multi-view clustering, which explores complementary information between multiple distinct feature sets for better clustering, has a wide range of applications, e.g., knowledge management and information retrieval. Traditional multi-view clustering methods usually assume that all examples have complete feature sets.
Qiyue Yin, Shu Wu, Liang Wang 0001
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Unified subspace learning for incomplete and unlabeled multi-view data
Pattern Recognition, 2017Class indicator matrix is learned for incomplete and unlabeled multi-view data.Preserving the inter-view and intra-view data similarity can improve performance.Running time is in the same magnitudes with that of the mainstream methods.Obtain best results for incomplete multi-view clustering and cross-modal retrieval.
Qiyue Yin, Shu Wu
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Prototype Matching Learning for Incomplete Multi-View Clustering
IEEE Transactions on Image ProcessingAs information acquisition diversifies, data is acquired and stored in increasing modalities. However, sensor failures or equipment issues can lead to partial data loss in certain views, resulting in incomplete multi-view clustering (IMVC) problems. Although some prototype-based IMVC methods have achieved satisfactory performance, almost all of these ...
Honglin Yuan +6 more
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Consensus Graph Learning for Incomplete Multi-view Clustering
2019Multi-view data clustering is a fundamental task in current machine learning, known as multi-view clustering. Existing multi-view clustering methods mostly assume that each data instance is sampled in all views. However, in real-world applications, it is common that certain views miss number of data instances, resulting in incomplete multi-view data ...
Wei Zhou 0085 +2 more
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Incomplete Multi-view Clustering via Structured Graph Learning
2018In real applications, multi-view clustering with incomplete data has played an important role in the data mining field. How to design an algorithm to promote the clustering performance is a challenging problem. In this paper, we propose an approach with learned graph to handle the case that each view suffers from some missing information.
Jie Wu +4 more
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Adversarial learning for multi-view network embedding on incomplete graphs
Knowledge-Based Systems, 2019Abstract Network embedding, as a promising way of node representation learning, is capable of supporting various downstream network mining tasks, and has attracted growing research interests recently. Existing approaches mostly focus on learning the low-dimensional node representations by preserving the local or global topology information of a ...
Chaozhuo Li +5 more
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Community-Aware Multi-View Representation Learning With Incomplete Information
IEEE Transactions on Pattern Analysis and Machine IntelligenceDue to the complexity of data collection in the real world, Multi-view Representation Learning (MvRL) always encounters the incomplete information challenge, typically manifested as the Sample-missing Problem (SP) and the View-unaligned Problem (VP).
Haobin Li +4 more
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Robust Graph Contrastive Learning for Incomplete Multi-view Clustering
Proceedings of the Thirty-Fourth International Joint Conference on Artificial IntelligenceIn recent years, multi-view clustering (MVC) has become a promising approach for analyzing heterogeneous multi-source data. However, during the collection of multi-view data, factors such as environmental interference or sensor failure often lead to the loss of view sample data, resulting in incomplete multi-view clustering (IMVC).
Deyin Zhuang +5 more
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