Deep Convolutional Transform Learning -- Extended version

• URL: http://arxiv.org/abs/2010.01011v1
• Date: Fri, 2 Oct 2020 14:03:19 GMT
• Title: Deep Convolutional Transform Learning -- Extended version
• Authors: Jyoti Maggu and Angshul Majumdar and Emilie Chouzenoux and Giovanni Chierchia
• Abstract summary: 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.
• Score: 31.034188573071898
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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