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Incomplete Multi-view Multi-label Active Learning

2021 IEEE International Conference on Data Mining (ICDM), 2021
Chuanwei Qu +4 more
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Adversarial incomplete multi-view clustering with adaptive contrastive learning

Neural Networks
Incomplete multi-view data frequently arises in scenarios where certain views are unavailable due to sensor malfunctions or data corruption. In recent years, deep incomplete multi-view clustering has gained significant attention for its capacity to address such challenges.
Siyuan Peng +4 more
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Low-Rank Kernel Tensor Learning for Incomplete Multi-View Clustering

Proceedings of the AAAI Conference on Artificial Intelligence
Incomplete Multiple Kernel Clustering algorithms, which aim to learn a common latent representation from pre-constructed incomplete multiple kernels from the original data, followed by k-means for clustering. They have attracted intensive attention due to their high computational efficiency. However, our observation reveals that the imputation of these
Tingting Wu, Songhe Feng, Jiazheng Yuan
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Incomplete multi-view clustering via kernelized graph learning