I have a fully-connected neural network whose input is a vector of $n$ (normalized) integers. Each of these integers is associated with an object. The output is similarly a vector of $n$ values each of which is the prediction of a metric corresponding to one of those objects. The network is trained using the data of all $n$ objects.
Now, I need to consider the prediction scenario above in a faulty manner, say, a fault just eliminates one of those objects. Now, I am wondering whether or not I can use the very neural network above to predict the features of the remaining $n-1$ objects. In particular, I don't who what I need to feed into the input associated with the eliminated object.
Any thoughts and ideas are welcome!