I have a general question about supervised ANNs that map inputs to outputs. It is possible to vary the length of the input and output vectors by inserting some dummy variables that will not be considered in the mapping (or will be mapped to other dummy variables). So basically the mapping should look like this (v: value, d:dummy)
Input vector 1 $[v,v,v,v,v] \rightarrow$ Output vector 1 $[v,v,v,v,v]$
Input vector 2 $[v,v,v,v,v]\rightarrow$ Output vector 2 $[v,v,v,v,v]$
Input vector 3 $[v,v,v,d,d] \rightarrow$ Output vector 3 $[v,v,v,d,d]$
Input vector 4 $[v,v,d,d,d] \rightarrow$ Output vector 4 $[v,v,d,d,d]$
Input vector 5 $[v,d,d,d,d] \rightarrow$ Output vector 5 $[v,d,d,d,d]$
The input and output vectors have a length of 5 with 5 values. However, sometimes only a vector of size e.g. 3 (which is basically a vector of length 5 with 2 dummy variables) should be mapped to an output vector of length 3. So after training the ANN should know that if it for example get an input vector of length 3 it should produce an output vector of length 3.
Is something like this generally possible with ANNs or other machine learning approaches? If so, what type of ANN or machine learning approach can be used for this?
Reminder: Can anybody give me more insights into this? I'll appreciate every comment.
