Deep Convolutional Transform Learning -- Extended version
This is an incremental improvement for unsupervised learning in machine learning tasks like classification and clustering.
The authors tackled unsupervised representation learning by introducing Deep Convolutional Transform Learning (DCTL), which stacks convolutional transforms to learn independent kernels across layers, and the method outperformed its shallow version on benchmark datasets.
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.