Supervised Dimensionality Reduction and Classification with Convolutional Autoencoders. (arXiv:2208.12152v2 [cs.LG] UPDATED)

The joint optimization of the reconstruction and classification error is a
hard non convex problem, especially when a non linear mapping is utilized. In
order to overcome this obstacle, a novel optimization strategy is proposed, in
which a Convolutional Autoencoder for dimensionality reduction and a classifier
composed by a Fully Connected Network, are combined to simultaneously produce
supervised dimensionality reduction and predictions. It turned out that this
methodology can also be greatly beneficial in enforcing explainability of deep
learning architectures. …

arxiv classification dimensionality lg