Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.08557 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909388198903808 |
|---|---|
| author | Kleiber, Christian Oliver, William H. Buck, Tobias |
| author_facet | Kleiber, Christian Oliver, William H. Buck, Tobias |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_08557 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning Locally Adaptive Metrics that Enhance Structural Representation with $\texttt{LAMINAR}$ Kleiber, Christian Oliver, William H. Buck, Tobias Machine Learning 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. |
| title | Learning Locally Adaptive Metrics that Enhance Structural Representation with $\texttt{LAMINAR}$ |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2411.08557 |