How to combine a Self-organising map and a multilayer perceptron in python
Another way to use SOM is for vector quantisation. Rather than using the winning SOM coordinates, use the codebook values of the winning neuron. Not sure which articles you are reading, but I would have said that SOM into MLP will only provide better accuracy in certain cases. Also, you will need to choose parameters like dimensionality and map size wisely.
For image processing, I would have said that Autoencoders or Convolutional Neural Networks (CNNs) are more cutting-edge alternatives to SOM to investigate if you're not determined on the SOM + MLP architecture.
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?