Author： Giraldo Luis Lozano Fernando Quijano Nicanor
Publisher： Springer Publishing Company
ISSN： 0885-6125
Source： Machine Learning, Vol.82, Iss.1, 2011-01, pp. : 71-90
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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.
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