Graph Quantization
  Vector quantization(VQ) is a lossy data compression technique from signal
processing, which is restricted to feature vectors and therefore inapplicable
for combinatorial structures. This contribution presents a theoretical
foundation of graph quantization (GQ) that extends VQ to the domain of
attributed graphs. We present the necessary Lloyd-Max conditions for optimality
of a graph quantizer and consistency results for optimal GQ design based on
empirical distortion measures and stochastic optimization. These results
statistically justify existing clustering algorithms in the domain of graphs.
The proposed approach provides a template of how to link structural pattern
recognition methods other than GQ to statistical pattern recognition.
