  Subspace clustering methods based on expressing each data point as a linear
combination of all other points in a dataset are popular unsupervised learning
techniques. However, existing methods incur high computational complexity on
large-scale datasets as they require solving an expensive optimization problem
and performing spectral clustering on large affinity matrices. This paper
presents an efficient approach to subspace clustering by selecting a small
subset of the input data called landmarks. The resulting subspace clustering
method in the reduced domain runs in linear time with respect to the size of
the original data. Numerical experiments on synthetic and real data demonstrate
the effectiveness of our method.
