This paper presents a novel autoencoder with ordered variance (AEO) in which the loss function is modified with a variance regularization term to enforce order in the latent space. Further, the autoencoder is modified using ResNets, which results in a ResNet AEO (RAEO). The paper also illustrates the effectiveness of AEO and RAEO in extracting nonlinear relationships among input variables in an unsupervised setting.
@article{arxiv.2402.14031,
title = {Autoencoder with Ordered Variance for Nonlinear Model Identification},
author = {Midhun T. Augustine and Parag Patil and Mani Bhushan and Sharad Bhartiya},
journal= {arXiv preprint arXiv:2402.14031},
year = {2024}
}