I have data that I would like to interpret in higher dimension (sort of the opposite of an auto-encoder). What I intent to achieve is, an
m dimension data being represented in
n dimensions (
n>m) with the constraint that the representation in the
n dimensions is maximally separated.
For clarity, I will assume the binary field. My data has
m=20 dimensions. I would like to represent this data in a
n=50 dimensional space. There are
2^m number of data combinations that are possible and
2^n number of points in the larger dimension. My goal is to select
2^m points from
2^n points, which are as far away from each other as possible.
I am versed with neural networks. What I want to know is how do I tackle this problem from a neural network perspective? Thanks!