I am working on similarity calculation between entities of similar type. For each entity I am able to make a vector that comprises of multiple vectors itself.
- A = 50*1 vector
- B = 100*1 vector
- C = 50*1 vector
- D = [age, gender, x-feature, y-feature, z-feature]
Entity = [A B C D], basically concatenating all of those.
My problem is that: since they come from different spaces, each would have different orders of magnitude.
If I run KNN on these vectors, I doubt that results are going to be governed by features of one space only. What should be done to get best KNN results? What "normalisation" would be best here?