Learning Locally Adaptive Metrics that Enhance Structural Representation with $\texttt{LAMINAR}$
This addresses the need for better structural representation in data analysis for fields like physical sciences, though it appears incremental as it builds on existing metric learning approaches.
The paper tackles the problem of standard metrics failing to capture underlying structure in complex datasets by introducing LAMINAR, an unsupervised pipeline that produces a locally adaptive metric, resulting in more informative density-based distances compared to Euclidean metrics.
We present $\texttt{LAMINAR}$, a novel unsupervised machine learning pipeline designed to enhance the representation of structure within data via producing a more-informative distance metric. Analysis methods in the physical sciences often rely on standard metrics to define geometric relationships in data, which may fail to capture the underlying structure of complex data sets. $\texttt{LAMINAR}$ addresses this by using a continuous-normalising-flow and inverse-transform-sampling to define a Riemannian manifold in the data space without the need for the user to specify a metric over the data a-priori. The result is a locally-adaptive-metric that produces structurally-informative density-based distances. We demonstrate the utility of $\texttt{LAMINAR}$ by comparing its output to the Euclidean metric for structured data sets.