non-linear-pca

This paper introduces ODIN, a novel autoencoder architecture that enforces orthogonality and importance ordering of latent dimensions, recovering PCA-like interpretability in a fully non-linear regime. The method integrates geometric constraints into the training objective, theoretically grounded and empirically validated on synthetic and real-world datasets.