Mar 6, 2014

Unsupervised manifold learning of collective behavior

BioRxiv : the Preprint Server for Biology
Barry David JacobsonJames Roger Watson

Abstract

Collective behavior is an emergent property of numerous complex systems, from financial markets to cancer cells to predator-prey ecological systems. Characterizing modes of collective behavior is often done through human observation, training generative models, or other supervised learning techniques. Each of these cases requires knowledge of and a method for characterizing the macro-state(s) of the system. This presents a challenge for studying novel systems where there may be little prior knowledge. Here, we present a new unsupervised method of detecting emergent behavior in complex systems, and discerning between distinct collective behaviors. We require only metrics defined on the set of agents, which measure agents' nearness in variables of interest. We apply the method of diffusion maps to the systems to recover efficient embeddings of their interaction networks. Comparing these geometries, we formulate a measure of similarity between two networks, called the map alignment statistic (MAS). A large MAS is evidence that the two networks are codetermined in some fashion, indicating an emergent relationship between the metrics of nearness. Additionally, the form of the macro-scale organization is encoded in the covariances am...Continue Reading

  • References
  • Citations

References

  • We're still populating references for this paper, please check back later.
  • References
  • Citations

Citations

  • This paper may not have been cited yet.

Mentioned in this Paper

Environment
Experience
Left Cochlea
Simulation
Analysis
Specimen Type - Electrode
Labyrinth
Abnormal Shape
Cochlear Implants
Cochlear Structure

Related Feeds

BioRxiv & MedRxiv Preprints

BioRxiv and MedRxiv are the preprint servers for biology and health sciences respectively, operated by Cold Spring Harbor Laboratory. Here are the latest preprint articles (which are not peer-reviewed) from BioRxiv and MedRxiv.