  Subspace clustering is the classical problem of clustering a collection of
data samples that approximately lie around several low-dimensional subspaces.
The current state-of-the-art approaches for this problem are based on the
self-expressive model which represents the samples as linear combination of
other samples. However, these approaches require sufficiently well-spread
samples for accurate representation which might not be necessarily accessible
in many applications. In this paper, we shed light on this commonly neglected
issue and argue that data distribution within each subspace plays a critical
role in the success of self-expressive models. Our proposed solution to tackle
this issue is motivated by the central role of data augmentation in the
generalization power of deep neural networks. We propose two subspace
clustering frameworks for both unsupervised and semi-supervised settings that
use augmented samples as an enlarged dictionary to improve the quality of the
self-expressive representation. We present an automatic augmentation strategy
using a few labeled samples for the semi-supervised problem relying on the fact
that the data samples lie in the union of multiple linear subspaces.
Experimental results confirm the effectiveness of data augmentation, as it
significantly improves the performance of general self-expressive models.
