Now that I’ve got an easy to use (Max) native visualizer, I’ve been thinking about this idea that has come up a few times throughout the various plenaries and weekly chats.
The idea of taking a dimensionally reduced representation and then transforming it in one way or another.
Some of this is easier to explain with a video, so here’s a quick video explaining what I mean:
Some specific questions and use cases I’m talking about in the video are:
- Taking a 2D representation and remapping via changing/skewing the orientation. Some of this might be simple rotation stuff, but in the example I show in the video it’s not so straight forward. Perhaps this is a problem for a regressor or something? I’ve not really done this “point to point” training before.
- Having the 2D representation remapped onto a more perceptually aligned orientation (e.g. loudness/centroid on the axes). I don’t know if this is the same as simply mapping the original points according to those descriptors, but I don’t think it is as new relationships are formed via the dimensionality reduction. This would essentially be an “automatic” version of what I suggest in #1 above.
- Taking any of these 2D projections and distributing them evenly across the available space. I know @tedmoore has mentioned specific algorithms towards that end, it’s just a matter of what is possible to implement within Max (or with minimal friction from within Max). Ideally natively would be great since you can then transform incoming points in a similar way, but I envision this use case being more about using completely unrelated descriptors/parameters to navigate that space (MIDI controller, or X/Y grid like @tremblap and @spluta are so fond of).
- Lastly, this starts getting a bit more complicated, but a way to visually/graphically transform a 2D representation (ala Photoshop, stretching corners, bending/warping/etc…) such that you can take other 2D representations and place/stretch/map them onto another space. This is something that @b.hackbarth and @jamesbradbury have talked about a bit, and I can see it being super powerful, particularly when you are combining multiple corpora and/or sections/subsections of multiple corpora. @tremblap mentioned doing something like this via jitter, where you apply a visual transformation, and then just run each dataset through that function, but this starts getting a bit complicated (obviously).