Work

Adaptive Locally Aligned Ant Technique for Manifold Detection and Denoising

Year: 2025

Type: conference-paper

Abstract: The detection and extraction of noisy manifolds from data have various applications.In Astronomy, the detection of faint streams and filaments is particularly difficult due to background contamination, which immerses and hides them in noise.The biologically inspired Locally Aligned Ant Technique (LAAT) has been demonstrated as an efficient and flexible algorithm to detect and denoise versatile structures within noisy backgrounds.Our contribution extends LAAT two-fold: (1) introduction of a dynamic local radius, and (2) locally variable pheromone deposition.The former avoids highlighting spurious patterns in noisy regions and allows smaller jumps in areas with strong alignment.The latter increases pheromone deposition in fainter zones.We demonstrate this in 2 datasets.

Source: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)

DOI: 10.14428/esann/2025.es2025-185

Authors: Felipe Contreras, Kerstin Bunte, Reynier Peletier

Institutions: University of Groningen, University of Valparaíso

Language: English

Topic: Metaheuristic Optimization Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funders: Agenția Națională pentru Cercetare și Dezvoltare, CHIST-ERA

OpenAlex ID: W4409481149

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