Noisy Sparse Subspace Clustering
This addresses subspace clustering for noisy data in applications like computer vision, but it is incremental as it modifies an existing method.
The paper tackles subspace clustering under noise by studying Sparse Subspace Clustering (SSC) with adversarial or random noise, showing that a modified version is provably effective in correctly identifying underlying subspaces, extending theoretical guarantees to practical settings.
This paper considers the problem of subspace clustering under noise. Specifically, we study the behavior of Sparse Subspace Clustering (SSC) when either adversarial or random noise is added to the unlabelled input data points, which are assumed to be in a union of low-dimensional subspaces. We show that a modified version of SSC is \emph{provably effective} in correctly identifying the underlying subspaces, even with noisy data. This extends theoretical guarantee of this algorithm to more practical settings and provides justification to the success of SSC in a class of real applications.