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You are proposing that a reduced dimensional projection of a large dataset cannot approach this limit? I.e. expose the underlying low rank of nearly any huge s
by apathy 9y ago
You are proposing that a reduced dimensional projection of a large dataset cannot approach this limit?
I.e. expose the underlying low rank of nearly any huge sparse data matrix with an SVD or NMF. Enable fast recovery with a shitty (CS-wise) hash function. Recover most of the information about an observation's neighbors in a fraction of the time taken by many other approaches.
What's popular for ML benchmarking these days is not necessarily the same as what's needed for a specific application. It's a useful proof to keep in mind before prematurely optimizing with overly complicated approaches.
- highd 9y agoYou are of course free to try simple dimensionality reduction and nearest neighbors, and if that works on your problem that's fantastic. To the research community, though, problems where approaches like that work were considered "solved" decades ago. And of course, in industry, if there's a chance of that working it's tried. But no one's building self driving cars with PCA and LSH.