I've created a feedforward neural network using DL4J in Java.
Hypothetically and to keep things simple, assume this neural network is a binary classifier of squares and circles.
The input, a feature vector, would be composed of say... 5 different variables:
[number_of_corners,
number_of_edges,
area,
height,
width]
Now so far, my binary classifier can tell the two shapes apart quite well as I'm giving it a complete feature vector.
My question: is it possible to input only maybe 2 or 3 of these features? Or even 1? I understand results will be less accurate while doing so, I just need to be able to do so.
If it is possible, how?
How would I do it for a neural network with 213 different features in the input vector?