Foraging theory for dimensionality reduction of clustered data

Authors:
Luis Felipe Giraldo;Fernando Lozano;Nicanor Quijano
Affiliations:
Department of Electrical and Electronic Engineering, Universidad de los Andes, Bogotá, Colombia;Department of Electrical and Electronic Engineering, Universidad de los Andes, Bogotá, Colombia;Department of Electrical and Electronic Engineering, Universidad de los Andes, Bogotá, Colombia
Venue:
Machine Learning
Year:
2011

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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.