Recently, multi-view outlier detection attracts increasingly more attention. Although existing
multi-view outlier detection methods have demonstrated promising performance, they still …

Incomplete Multi-View Clustering (IMVC) is crucial for multi-media data analysis. While
graph learning-based IMVC methods have shown promise, they still have limitations. The …

Latent multi-view subspace clustering has been demonstrated to have desirable clustering
performance. However, the original latent representation method vertically concatenates the …

Multi-view unsupervised feature selection has gained significant attention in effectively
reducing the dimensionality of unlabeled data collected from multiple sources. Many existing …

Multi-view clustering is an important task in multimedia and machine learning. In multi-view
clustering, multi-view spectral clustering is one kind of the most popular and effective …

Multiple kernel clustering (MKC) has garnered considerable attention, as their efficacy in
handling nonlinear data in high-dimensional space. However, current MKC methods have …

The inherent variability and unpredictability in open multi-view learning scenarios infuse
considerable ambiguity into the learning and decision-making processes of predictors. This …

Deep multi-view clustering has been widely studied. However, since it is an unsupervised
task, where no labels are used to guide the training, it is still unreliable especially when …

Multi-view unsupervised feature selection (MUFS) has attracted considerable attention as an
efficient dimensionality reduction technique. Data usually exhibit certain correlations, and in …

With the growing diversity of data sources, multi-view learning methods have attracted
considerable attention. Among these, by modeling the multi-view data as multi-view graphs …