  This paper addresses unsupervised representation learning on tabular data
containing multiple views generated by distinct sources of measurement.
Traditional methods, which tackle this problem using the multi-view framework,
are constrained by predefined assumptions that assume feature sets share the
same information and representations should learn globally shared factors.
However, this assumption is not always valid for real-world tabular datasets
with complex dependencies between feature sets, resulting in localized
information that is harder to learn. To overcome this limitation, we propose a
data-driven approach that learns feature set dependencies by representing
feature sets as graph nodes and their relationships as learnable edges.
Furthermore, we introduce LEGATO, a novel hierarchical graph autoencoder that
learns a smaller, latent graph to aggregate information from multiple views
dynamically. This approach results in latent graph components that specialize
in capturing localized information from different regions of the input, leading
to superior downstream performance.

Machine Learning