Illustration of the LGB Vector Quantization Algorithm

1. Training vectors as points in a 2D space
2. First step, the centroid of the entire set
3. Split the centroid into two (randomly)
4. Nearest-neighbor search, result in 2 clusters
5. Update the centroid from each cluster
6. Repeat nearest-neighbor search
7. And repeat centroid update
8. Until the centroids are stable (end of the inner loop)
9. Repeat outer loop, double the number of centroids by splitting
10. Iterate inner loop until the centroids are stable
11. Continue...
12. 8 centroids
13. And...
14. Finally, stop when the desired number of centroids is obtained