  Deep Subspace Clustering Networks (DSC) provide an efficient solution to the
problem of unsupervised subspace clustering by using an undercomplete deep
auto-encoder with a fully-connected layer to exploit the self expressiveness
property. This method uses undercomplete representations of the input data
which makes it not so robust and more dependent on pre-training. To overcome
this, we propose a simple yet efficient alternative method - Overcomplete Deep
Subspace Clustering Networks (ODSC) where we use overcomplete representations
for subspace clustering. In our proposed method, we fuse the features from both
undercomplete and overcomplete auto-encoder networks before passing them
through the self-expressive layer thus enabling us to extract a more meaningful
and robust representation of the input data for clustering. Experimental
results on four benchmark datasets show the effectiveness of the proposed
method over DSC and other clustering methods in terms of clustering error. Our
method is also not as dependent as DSC is on where pre-training should be
stopped to get the best performance and is also more robust to noise. Code -
\href{https://github.com/jeya-maria-jose/Overcomplete-Deep-Subspace-Clustering}{https://github.com/jeya-maria-jose/Overcomplete-Deep-Subspace-Clustering
