I would like to try extracting "filters" (similar to the facial feature extraction) from a multi-task three hidden-layer neural network with ReLU activations. My data is a 1-D vector of counts and I'd like to extract higher level features. However, I'm having trouble finding any resources on feature extraction from non-convolutional neural networks beyond the first layer. Is this a thing that makes sense and does anybody know any papers or know a formula I could implement to find a ranking of bin importance for a particular node in a hidden layer?
Things I've been thinking of using, but am not sure if I'm on the right track:
- gradient ascent from random perturbation of the input vector
- looking at inputs that activate the data the best and then masking out data