  A growing awareness of multi-view learning as an important component in data
science and machine learning is a consequence of the increasing prevalence of
multiple views in real-world applications, especially in the context of
networks. In this paper we introduce a new scalability framework for multi-view
subspace clustering. An efficient optimization strategy is proposed, leveraging
kernel feature maps to reduce the computational burden while maintaining good
clustering performance. The scalability of the algorithm means that it can be
applied to large-scale datasets, including those with millions of data points,
using a standard machine, in a few minutes. We conduct extensive experiments on
real-world benchmark networks of various sizes in order to evaluate the
performance of our algorithm against state-of-the-art multi-view subspace
clustering methods and attributed-network multi-view approaches.
