NeurIPS 2020

Regularized linear autoencoders recover the principal components, eventually

Meta Review

This paper investigates ways for the regularized linear autoencoder to recover the original principal components of a matrix, and it shows that non-uniform L2 regularization and nested dropout lead to such recovery, ordinary GD using these objectives suffers from slow convergence, a new alternative optimization algorithm can accelerate convergence, and this new algorithm is connected to a Hebbian algorithm. The paper is well written and makes a nice contribution. All reviewers were positive, and several reviewers improved their scores in light of the author responses and subsequent discussion.