Learning Ordered Representations in Latent Space for Intrinsic Dimension Estimation via Principal Component Autoencoder
This work addresses the challenge of preserving ordered representations in nonlinear autoencoders for researchers in machine learning, though it appears incremental as it builds on prior linear methods.
The paper tackles the problem of nonlinear dimensionality reduction by proposing a novel autoencoder framework that integrates non-uniform variance regularization with an isometric constraint, achieving ordered representations and variance retention as a natural generalization of PCA.
Autoencoders have long been considered a nonlinear extension of Principal Component Analysis (PCA). Prior studies have demonstrated that linear autoencoders (LAEs) can recover the ordered, axis-aligned principal components of PCA by incorporating non-uniform $\ell_2$ regularization or by adjusting the loss function. However, these approaches become insufficient in the nonlinear setting, as the remaining variance cannot be properly captured independently of the nonlinear mapping. In this work, we propose a novel autoencoder framework that integrates non-uniform variance regularization with an isometric constraint. This design serves as a natural generalization of PCA, enabling the model to preserve key advantages, such as ordered representations and variance retention, while remaining effective for nonlinear dimensionality reduction tasks.