  This work introduces a new unsupervised representation learning technique
called Deep Convolutional Transform Learning (DCTL). By stacking convolutional
transforms, our approach is able to learn a set of independent kernels at
different layers. The features extracted in an unsupervised manner can then be
used to perform machine learning tasks, such as classification and clustering.
The learning technique relies on a well-sounded alternating proximal
minimization scheme with established convergence guarantees. Our experimental
results show that the proposed DCTL technique outperforms its shallow version
CTL, on several benchmark datasets.
