Hi dear colleagues!
I am currently involved in a project where we are working with a neural model.
The point is that we are starting to train our model with a sparse input (sparse matrix) and I cannot find the best way to preprocess the dataset. Even I am not sure if it is better to use the sparse input or a pre-processed input.
In one hand the dataset sparsity is about 80% to 90% and on other hands, dataset sparsity reaches 20 %. Datasets have the same origin but were picked up in different environmental conditions.
So far, I have trained my model just with the columns with nonzeros values but I realize that it is a waste of information do that.
Here is where my question comes, how could I train my neural network with a sparse input? and what set of methods can I implement to improve the performance of my NN with this kind of input?