  This paper focuses on the sparse subspace clustering problem, and develops an
online algorithmic solution to cluster data points on-the-fly, without
revisiting the whole dataset. The strategy involves an online solution of a
sparse representation (SR) problem to build a (sparse) dictionary of
similarities where points in the same subspace are considered "similar,"
followed by a spectral clustering based on the obtained similarity matrix. When
the SR cost is strongly convex, the online solution converges to within a
neighborhood of the optimal time-varying batch solution. A dynamic regret
analysis is performed when the SR cost is not strongly convex.
