Hierarchical Clustering of Sensorimotor Features
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Abstract:
In this paper a method for clustering patterns represented by sets of sensorimotor features is introduced. Sensorimotor features as a biologically inspired representation have proofed to be working for the recognition task, but a method for unsupervised learning of classes from a set of patterns has been missing yet. By utilization of Self-Organizing Maps as a intermediate step, a hierarchy can be build with standard agglomerative clustering methods.
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PDF URL: http://dx.doi.org/10.1007/978-3-642-04617-9_42
Reference:
Hierarchical Clustering of Sensorimotor Features (Konrad Gadzicki), Chapter in KI 2009: Advances in Artificial Intelligence, Springer Science + Business Media, 2009.
Bibtex Entry:
@InCollection{Gadzicki2009,
  author    = {Konrad Gadzicki},
  title     = {Hierarchical Clustering of Sensorimotor Features},
  booktitle = {{KI} 2009: Advances in Artificial Intelligence},
  publisher = {Springer Science + Business Media},
  year      = {2009},
  pages     = {331--338},
  abstract  = {In this paper a method for clustering patterns represented by sets of sensorimotor features is introduced. Sensorimotor features as a biologically inspired representation have proofed to be working for the recognition task, but a method for unsupervised learning of classes from a set of patterns has been missing yet. By utilization of Self-Organizing Maps as a intermediate step, a hierarchy can be build with standard agglomerative clustering methods.},
  doi       = {10.1007/978-3-642-04617-9_42},
  keywords  = {former_inproceedings},
  url       = {10.1007/978-3-642-04617-9_42">http://dx.doi.org/10.1007/978-3-642-04617-9_42},
}