Graph Quantization

Details Graph Quantization Graph Quantization

2010-01-06

arxiv.org/abs/1001.0921v1

Computer Science

Artificial Intelligence

24 pages; submitted to CVIU

Scientific paper

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.

Affiliated with

Also associated with

No associations

Rating

Graph Quantization does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Graph Quantization, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Graph Quantization will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-164439