Foraging Theory for Dimensionality Reduction of Clustered Data
Abstract
We present a bioinspired algorithm which performs dimensionality reduction on datasets for visual exploration, under the assumption that they have a clustered structure. We formulate a decision-making strategy based on foraging theory, where a software agent is viewed as an animal, a discrete space as the foraging landscape, and objects representing points from the dataset as nutrients or prey items. We apply this algorithm to artificial and real databases, and show how a multi-agent system addresses the problem of mapping high-dimensional data into a two-dimensional space.
Cite
Text
Giraldo et al. "Foraging Theory for Dimensionality Reduction of Clustered Data." Machine Learning, 2011. doi:10.1007/S10994-009-5156-0Markdown
[Giraldo et al. "Foraging Theory for Dimensionality Reduction of Clustered Data." Machine Learning, 2011.](https://mlanthology.org/mlj/2011/giraldo2011mlj-foraging/) doi:10.1007/S10994-009-5156-0BibTeX
@article{giraldo2011mlj-foraging,
title = {{Foraging Theory for Dimensionality Reduction of Clustered Data}},
author = {Giraldo, Luis Felipe and Lozano, Fernando and Quijano, Nicanor},
journal = {Machine Learning},
year = {2011},
pages = {71-90},
doi = {10.1007/S10994-009-5156-0},
volume = {82},
url = {https://mlanthology.org/mlj/2011/giraldo2011mlj-foraging/}
}