Assume that there are
- data set A (known sample information).
- data set B (unknown sample information).
What I want to achieve is:
Sort each sample of the data set A in a two-dimensional space according to a certain rule, that is, make it representable in two-dimensional coordinates.
(According to the degree of similarity, correlation, etc. between the samples)
When a sample is extracted from the data set B and input to the deep learning model, the model can search in the data set A to obtain a sample (set) that has a certain correlation with the input sample.
How can I achieve it?