  A plethora of multi-view subspace clustering (MVSC) methods have been
proposed over the past few years. Researchers manage to boost clustering
accuracy from different points of view. However, many state-of-the-art MVSC
algorithms, typically have a quadratic or even cubic complexity, are
inefficient and inherently difficult to apply at large scales. In the era of
big data, the computational issue becomes critical. To fill this gap, we
propose a large-scale MVSC (LMVSC) algorithm with linear order complexity.
Inspired by the idea of anchor graph, we first learn a smaller graph for each
view. Then, a novel approach is designed to integrate those graphs so that we
can implement spectral clustering on a smaller graph. Interestingly, it turns
out that our model also applies to single-view scenario. Extensive experiments
on various large-scale benchmark data sets validate the effectiveness and
efficiency of our approach with respect to state-of-the-art clustering methods.
