DeepAI AI Chat
Log In Sign Up

Deep Convolutional Transform Learning – Extended version

by   Jyoti Maggu, et al.

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.


page 1

page 2

page 3

page 4


DeConFuse : A Deep Convolutional Transform based Unsupervised Fusion Framework

This work proposes an unsupervised fusion framework based on deep convol...

Towards Deep Representation Learning with Genetic Programming

Genetic Programming (GP) is an evolutionary algorithm commonly used for ...

A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directions

Clustering is a fundamental machine learning task which has been widely ...

Use of Deterministic Transforms to Design Weight Matrices of a Neural Network

Self size-estimating feedforward network (SSFN) is a feedforward multila...

Towards Explainable Convolutional Features for Music Audio Modeling

Audio signals are often represented as spectrograms and treated as 2D im...

Do Deep Convolutional Nets Really Need to be Deep and Convolutional?

Yes, they do. This paper provides the first empirical demonstration that...

Spectral Analysis Network for Deep Representation Learning and Image Clustering

Deep representation learning is a crucial procedure in multimedia analys...