With a unit test and assignment this week, there were no new topics introduced. The research I had to do for SOMs in relation to the assignment did yield a lot of new information.
Implementation of SOM networks has a number variable components:
- Neighbour updating, neighbour radius
- Weight decay
- Random weight initialization (the random weights at initialization will effect clusters)
- Adjusting learning rate and learning rate decay
- Adjusting training/test data split
- Adjusting number of neurons in 2D lattice
Evaluation of the quality of clusters created by a SOM is quite difficult. Weight distances is the best method for checking if like clusters have formed in different sections of the map. Running some clustering in MatLab yielded basic results but I am not familiar enough with the clustering tool to extrapolate all of of the information required to make inference on the results.
The information gained from self-organizing maps may be useful when constructing supervised learning networks.