I am fairly new to data science and would like to know in simple words (like teaching your grandmother) what the difference between metric and non-metric Multidimensional scaling is.
I have been googling for 2 days and watching different videos and wasn't able to quite understand some of the terms people are using to describe the difference, maybe I am lacking some basic knowledge but I don't know in which area so if you have an idea of what I should have a firm understanding of before tackling this subject, I would appreciate the advice. Here is what I know:
Multidimensional scaling is a way of reducing dimensions to be able to visualize or represent data in a more friendly manner. I know that there are several ways for MDS like metric and non metric, PCA and FA (maybe FA is a part of PCA, I'm not sure).
The example I am trying to apply this on is a set of data showing different cities and attributes related to these cities. For example, on a score from 1-7 (1 lowest - 7 highest), this is the score of each city and the corresponding attribute.
**Clean** **Friendly** **Expensive** **Beautiful**
Berlin----------- 4 --------------------- 2-----------------------5------------------------6
Geneva---------6 --------------------- 3-----------------------7------------------------7
Paris------------ 3 --------------------- 4-----------------------6------------------------7
Barcelona----- 2 --------------------- 6-----------------------3------------------------4
How do I know if I should be using metric or non-metric MDS. Are there general rules of thumb or simple logic that I can use to decide without going deep into the technical process.
Thank you