  Graph-based subspace clustering methods have exhibited promising performance.
However, they still suffer some of these drawbacks: encounter the expensive
time overhead, fail in exploring the explicit clusters, and cannot generalize
to unseen data points. In this work, we propose a scalable graph learning
framework, seeking to address the above three challenges simultaneously.
Specifically, it is based on the ideas of anchor points and bipartite graph.
Rather than building a $n\times n$ graph, where $n$ is the number of samples,
we construct a bipartite graph to depict the relationship between samples and
anchor points. Meanwhile, a connectivity constraint is employed to ensure that
the connected components indicate clusters directly. We further establish the
connection between our method and the K-means clustering. Moreover, a model to
process multi-view data is also proposed, which is linear scaled with respect
to $n$. Extensive experiments demonstrate the efficiency and effectiveness of
our approach with respect to many state-of-the-art clustering methods.
