How to combine a Self-organising map and a multilayer perceptron in python
I have been wondering if there is any mileage to training a separate supervised neural network for the inputs which map to each node in the SOM. You'd then have separate supervised learning on the subset of the input data mapping to each SOM node. The networks attached to each node would perhaps be smaller and more easily trained than one large network which had to deal with the whole input space. There may also be benefit from including input vectors which map to the adjacent SOM nodes.
Is anyone aware of this being the subject of research?
I am working on a image recognition project in python. I have read in journals that if clustering performed by a self-organizing map (SOM) is input into a supervised neural network the accuracy of image recognition improves as opposed to the supervised network on its own. I have tried this myself by using the SOM to perform clustering and then using the coordinates of the winning neuron after each iteration as input to a multilayer perceptron from the keras. However the accuracy is very poor.
What output of SOM should be used as input to a multilayer perceptron?