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 …

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 …

Multi-view clustering is widely used to improve clustering performance. Recently, the
subspace clustering tensor learning method based on Markov chain is a crucial branch of …

With advances in information acquisition technologies, multi-view data become ubiquitous.
Multi-view learning has thus become more and more popular in machine learning and data …

Multi-view clustering is an important approach for analyzing multi-view data in an
unsupervised way. Among various methods, the multi-view subspace clustering approach …

Multi-view clustering, which aims to cluster datasets with multiple sources of information, has
a wide range of applications in the communities of data mining and pattern recognition …

Multi-view clustering is an important and challenging task in machine learning and data
mining. In the past decade, this topic attracted much attention and there have been many …

The k-means algorithm is a widely used method that starts with an initial partitioning of the
data and then iteratively converges towards the local solution by reducing the Sum of …

Multi-view data with each view corresponding to a type of feature set are common in real
world. Usually, previous multi-view learning methods assume complete views. However …