I'm Having a ML problem where my data set contains 60 features labelled into 3 groups (0, 1, -1).

I want to plot the data on a 2D surface to see how "close" (similar) data with `label x` is to data with `label y`, how the data spreads, are the labels separable, etc.

I was thinking about using *PCA* and transform the data from 80D to 2D, but It only retain 40% of the variance!

 - Is this a good approach for the problem? 
 - If so, does 40% suffice?
 - Are there any other/better approach for this?

EDIT:
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Plotting is not the main issue. The transformation from 80D to 2D (for an easy visialization) is whats difficult. 

Also, all of this is being made to know how much samples with `label 1` differs from `label 0` and `label -1` and **vice versa** (based on those original 80 features).

If there's a different method, that is not visualizing the "answer", I'll also be happy to hear about it!