As I am three weeks into my REU, I can definitely post exactly what I’m doing since I have a pretty good feeling for it.
So basically I am working on two main things:
- Help design and implement unsupervised learning into the current model for DIVA, the professor’s NN.
- Figure out exactly what sort of dimensional reduction occurs within the hidden node architecture of DIVA. Is it similar to principle component analysis? Or maybe even some other form of feature space dimensionality reduction? So determine what this is, and hopefully, with a little luck, be able to formalize it.
What I’d say the coolest implication of this is right now, for me anyway, besides the epistemological values, is seeing what type of implications this has for machine learning. To do this, we are currently talking about taking on an insanely challenging dataset, the netflix dataset.
We shall see where that ends up! Realistically the top contenders (BellKor and BigChaos) are ridiculously close and their progress seems to be slowing of late. BellKor is particularly impressive, with a 9.15% improvement (10% is the prize) over Netflix’s current algorithm.
I would be ecstatic to even submit an entry. Realistically speaking… well I don’t have a clue how realistic this is, but you have to shoot for the stars to hit the moon, eh?
