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Doubly Aligned Incomplete Multi-view Clustering
Specifically, on the one hand, DAIMC utilizes the given instance alignment information to learn a common latent feature matrix for all the views.
One-Pass Incomplete Multi-view Clustering
Real data are often with multiple modalities or from multiple heterogeneous sources, thus forming so-called multi-view data, which receives more and more attentions in machine learning.
Multi-target Unsupervised Domain Adaptation without Exactly Shared Categories
Unsupervised domain adaptation (UDA) aims to learn the unlabeled target domain by transferring the knowledge of the labeled source domain.
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