Topologically Regularized Data Embeddings

TL;DR

This paper introduces new topological loss functions to improve unsupervised data embeddings by incorporating prior topological knowledge, addressing limitations of existing methods, and demonstrating their effectiveness on synthetic and real datasets.

Contribution

The paper proposes a novel set of topological losses that naturally represent simple models and preserve original data structure, enhancing embedding quality.

Findings

Improved embedding quality on synthetic data.

Effective modeling of high-dimensional single-cell data.

Versatile application to graph embedding.

Abstract

Unsupervised feature learning often finds low-dimensional embeddings that capture the structure of complex data. For tasks for which prior expert topological knowledge is available, incorporating this into the learned representation may lead to higher quality embeddings. For example, this may help one to embed the data into a given number of clusters, or to accommodate for noise that prevents one from deriving the distribution of the data over the model directly, which can then be learned more effectively. However, a general tool for integrating different prior topological knowledge into embeddings is lacking. Although differentiable topology layers have been recently developed that can (re)shape embeddings into prespecified topological models, they have two important limitations for representation learning, which we address in this paper. First, the currently suggested topological losses fail to represent simple models such as clusters and flares in a natural manner. Second, these losses neglect all original structural (such as neighborhood) information in the data that is useful for learning. We overcome these limitations by introducing a new set of topological losses, and proposing their usage as a way for topologically regularizing data embeddings to naturally represent a prespecified model. We include thorough experiments on synthetic and real data that highlight the usefulness and versatility of this approach, with applications ranging from modeling high-dimensional single-cell data, to graph embedding.