A Variational Manifold Embedding Framework for Nonlinear Dimensionality Reduction

TL;DR

This paper introduces a variational manifold embedding framework for nonlinear dimensionality reduction that enhances flexibility over PCA and improves interpretability by linking solutions to partial differential equations.

Contribution

It proposes a novel variational framework that generalizes PCA to nonlinear embeddings and provides analytical characterizations of solutions, including symmetry properties.

Findings

Framework allows nonlinear embeddings surpassing PCA.

Solutions satisfy PDEs, aiding interpretability.

Special case recovers PCA exactly.

Abstract

Dimensionality reduction algorithms like principal component analysis (PCA) are workhorses of machine learning and neuroscience, but each has well-known limitations. Variants of PCA are simple and interpretable, but not flexible enough to capture nonlinear data manifold structure. More flexible approaches have other problems: autoencoders are generally difficult to interpret, and graph-embedding-based methods can produce pathological distortions in manifold geometry. Motivated by these shortcomings, we propose a variational framework that casts dimensionality reduction algorithms as solutions to an optimal manifold embedding problem. By construction, this framework permits nonlinear embeddings, allowing its solutions to be more flexible than PCA. Moreover, the variational nature of the framework has useful consequences for interpretability: each solution satisfies a set of partial differential equations, and can be shown to reflect symmetries of the embedding objective. We discuss these features in detail and show that solutions can be analytically characterized in some cases. Interestingly, one special case exactly recovers PCA.