Improved LAAT strategy to recover manifolds embedded in strong noise

2023

The automatic detection, extraction, and modeling of manifold structures from large data-sets is of great interest, especially in Astronomy. Existing manifold learning techniques for feature extraction in Computer Vision, Bioinformatics and signal denoising typically fail in astronomical scenarios, since they mostly assume low levels of noise and one manifold of fixed dimension. Therefore, the Locally Aligned Ant Technique (LAAT) was recently proposed to discover multiple faint and noisy structures of varying dimensionality embedded in large amounts of background noise. Although it demonstrates excellent results in multiple scenarios, its performance depends on global thresholding and user tuning. Here, we improve LAAT and replace the global threshold by a flexible local strategy.

@inproceedings{Contreras2023,
title = {Improved LAAT strategy to recover manifolds embedded in strong noise},
author = {Felipe Contreras and Reynier Peletier and Kerstin Bunte},
month = {4-6 October},
organization = {},
editor = {Michel Verleysen},
booktitle = {Proc. of the  31th "European Symposium on Artificial Neural Networks (ESANN)},
year = {2023},
publisher = {i6doc.com},
address = {Bruges (Belgium) and online event},
pages = {},
abstract = {The automatic detection, extraction, and modeling of manifold structures from large data-sets is of great interest, especially in Astronomy. Existing manifold learning techniques for feature extraction in Computer Vision, Bioinformatics and signal denoising typically fail in astronomical scenarios, since they mostly assume low levels of noise and one manifold of fixed dimension. Therefore, the Locally Aligned Ant Technique (LAAT) was recently proposed to discover multiple faint and noisy structures of varying dimensionality embedded in large amounts of background noise. Although it demonstrates excellent results in multiple scenarios, its performance depends on global thresholding and user tuning. Here, we improve LAAT and replace the global threshold by a flexible local strategy.},
doi = {},
url = {},
isbn = {},
}