Dissecting embedding method: learning higher-order structures from data

TL;DR

This paper investigates the limitations of current geometric deep learning methods in capturing complex higher-order data relationships and proposes a hypergraph-based framework for more accurate embedding analysis.

Contribution

It introduces a novel hypergraph theory approach to analyze embedding methods, addressing the limitations of graph-based assumptions and dissecting their potential inconsistencies.

Findings

Hypergraph framework captures higher-order data structures more effectively.

Analysis reveals limitations of traditional graph-based embeddings.

Application to arXiv data demonstrates improved data representation.

Abstract

Active area of research in AI is the theory of manifold learning and finding lower-dimensional manifold representation on how we can learn geometry from data for providing better quality curated datasets. There are however various issues with these methods related to finding low-dimensional representation of the data, the so-called curse of dimensionality. Geometric deep learning methods for data learning often include set of assumptions on the geometry of the feature space. Some of these assumptions include pre-selected metrics on the feature space, usage of the underlying graph structure, which encodes the data points proximity. However, the later assumption of using a graph as the underlying discrete structure, encodes only the binary pairwise relations between data points, restricting ourselves from capturing more complex higher-order relationships, which are often often present in various systems. These assumptions together with data being discrete and finite can cause some generalisations, which are likely to create wrong interpretations of the data and models outputs. Hence overall this can cause wrong outputs of the embedding models themselves, while these models being quite and trained on large corpora of data, such as BERT, Yi and other similar models.The objective of our research is twofold, first, it is to develop the alternative framework to characterize the embedding methods dissecting their possible inconsistencies using combinatorial approach of higher-order structures which encode the embedded data. Second objective is to explore the assumption of the underlying structure of embeddings to be graphs, substituting it with the hypergraph and using the hypergraph theory to analyze this structure. We also demonstrate the embedding characterization on the usecase of the arXiv data.