Autoencoder with Ordered Variance for Nonlinear Model Identification
Midhun T. Augustine Parag Patil Mani Bhushan Sharad Bhartiya
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.