  In many real-world applications, we have access to multiple views of the
data, each of which characterizes the data from a distinct aspect. Several
previous algorithms have demonstrated that one can achieve better clustering
accuracy by integrating information from all views appropriately than using
only an individual view. Owing to the effectiveness of spectral clustering,
many multi-view clustering methods are based on it. Unfortunately, they have
limited applicability to large-scale data due to the high computational
complexity of spectral clustering. In this work, we propose a novel multi-view
spectral clustering method for large-scale data. Our approach is structured
under the guided co-training scheme to fuse distinct views, and uses the
sampling technique to accelerate spectral clustering. More specifically, we
first select $p$ ($\ll n$) landmark points and then approximate the
eigen-decomposition accordingly. The augmented view, which is essential to
guided co-training process, can then be quickly determined by our method. The
proposed algorithm scales linearly with the number of given data. Extensive
experiments have been performed and the results support the advantage of our
method for handling the large-scale multi-view situation.
