Clustering is a machine learning paradigm of dividing sample subjects into a number of
groups such that subjects in the same groups are more similar to those in other groups. With …

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 …

Multi-view graph-based clustering aims to provide clustering solutions to multi-view data.
However, most existing methods do not give sufficient consideration to weights of different …

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 …

In the big data era, the data are generated from different sources or observed from different
views. These data are referred to as multi-view data. Unleashing the power of knowledge in …

A panoply of multi-view clustering algorithms has been developed to deal with prevalent
multi-view data. Among them, spectral clustering-based methods have drawn much attention …

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 …

Multiview clustering has gained increasing attention recently due to its ability to deal with
multiple sources (views) data and explore complementary information between different …