Recent high throughput experimental methods have been used to collect large biomedical
omics datasets. Clustering of single omic datasets has proven invaluable for biological and …

Multi-view learning is an emerging direction in machine learning which considers learning
with multiple views to improve the generalization performance. Multi-view learning is also …

RGB-D saliency detection has attracted increasing attention, due to its effectiveness and the
fact that depth cues can now be conveniently captured. Existing works often focus on …

Subspace clustering is an effective method that has been successfully applied to many
applications. Here, we propose a novel subspace clustering model for multi-view data using …

In this paper, we propose a novel Latent Multi-view Subspace Clustering (LMSC) method,
which clusters data points with latent representation and simultaneously explores underlying …

Multiview clustering algorithms have attracted intensive attention and achieved superior
performance in various fields recently. Despite the great success of multiview clustering …

Previous multi-view clustering algorithms mostly partition the multi-view data in their original
feature space, the efficacy of which heavily and implicitly relies on the quality of the original …

In this paper, we focus on how to boost the multi-view clustering by exploring the
complementary information among multi-view features. A multi-view clustering framework …

Due to the efficiency of learning relationships and complex structures hidden in data, graph-
oriented methods have been widely investigated and achieve promising performance …

Although multi-view learning has made significant progress over the past few decades, it is
still challenging due to the difficulty in modeling complex correlations among different views …