LAAT: Locally Aligned Ant Technique for discovering multiple faint low dimensional structures of varying density

Albolfazl Taghribi*, Kerstin Bunte, Rory Smith, Jihye Shin, Michele Mastropietro, Reynier Peletier, Peter Tino

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)
77 Downloads (Pure)


Dimensionality reduction and clustering are often used as preliminary steps for many complex machine learning tasks. The presence of noise and outliers can deteriorate the performance of such preprocessing and therefore impair the subsequent analysis tremendously. In manifold learning, several studies indicate solutions for removing background noise or noise close to the structure when the density is substantially higher than that exhibited by the noise. However, in many applications, including astronomical datasets, the density varies alongside manifolds that are buried in a noisy background. We propose a novel method to extract manifolds in the presence of noise based on the idea of Ant colony optimization. In contrast to the existing random walk solutions, our technique captures points that are locally aligned with major directions of the manifold. Moreover, we empirically show that the biologically inspired formulation of ant pheromone reinforces this behavior enabling it to recover multiple manifolds embedded in extremely noisy data clouds. The algorithm performance in comparison to state-of-the-art approaches for noise reduction in manifold detection and clustering is demonstrated, on several synthetic and real datasets, including an N-body simulation of a cosmological volume.
Original languageEnglish
Pages (from-to)6014-6027
Number of pages14
Journalieee transactions on knowledge and data engineering
Issue number6
Early online date23-May-2022
Publication statusPublished - 1-Jun-2023


  • Ant algorithm
  • Markov chain
  • multiple manifold learning
  • evolutionary computation


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