Topological methods for unsupervised learning problems [video] • Hacker News Board

Re: Topological methods for unsupervised learning problems [video]

#11

Post by nimithryn » Thu, Feb 28, 2019, 11:06 PM UTC

kaitai wrote: post #9

ham_sandwich wrote: post #3

I’ve been interested in learning about topological data analysis, haven’t dug in too deep yet, but it definitely looks like an interesting direction to zig in while the field at large zags with ever larger deep learning architectures. UMAP has already demonstrated its efficacy as a tool in any data scientist’s belt. Ayasdi and Gunnar Carlson’s work is certainly interesting, but unsure how much business value it can a…

I've been playing with topological methods for data analysis recently and I think there are some fruitful things happening. Seems like there are some ideas emerging from theory to practice which might be useful (Betti curves, multiparameter persistence, etc) but they're not quite there yet. Another idea that's been intriguing me lately is applied sheaf theory. Robinson, Ghrist, and Curry are the only people I see wor…

Look into OpenCog - there's some sheaf-theoretic NLP stuff going on there. There's a recent high-level overview by Linas Vepstas you can find on the ArXiv somewhere. There's a project called SheafSystem also, which is a sheaf-based database for scientific computing (I've never used it). I have some ideas I'm working on in this area also (not affiliated with these parties in any way, and the ideas are not ready to share yet, unfortunately.

What's your background, out of curiosity?

Re: Topological methods for unsupervised learning problems [video]

#12

Post by starchild_3001 » Fri, Mar 01, 2019, 4:17 AM UTC

I do a lot of clustering professionally, yet TDA feels very academic. Does finding locally connected components have practical value with "noisy" data sets'? If what you're after is locally connected components, why can't you use density clustering? Also my general feeling: If you have such weird shapes in R^n, maybe you should try to develop a better distance metric (vs finding connected components)? Just saying.

Re: Topological methods for unsupervised learning problems [video]

#13

Post by mikhailfranco » Fri, Mar 01, 2019, 7:47 PM UTC

ham_sandwich wrote: post #3

I’ve been interested in learning about topological data analysis, haven’t dug in too deep yet, but it definitely looks like an interesting direction to zig in while the field at large zags with ever larger deep learning architectures. UMAP has already demonstrated its efficacy as a tool in any data scientist’s belt. Ayasdi and Gunnar Carlson’s work is certainly interesting, but unsure how much business value it can a…

Some people think it can unlock at least $106m of business value: https://www.crunchbase.com/organization/ayasdi#section-overv...

Re: Topological methods for unsupervised learning problems [video]

#14

Post by mikhailfranco » Fri, Mar 01, 2019, 7:51 PM UTC

Great introductory talk. I have a physics and 3D graphics background, with just enough topology and sketchy CT to understand all the words, so now I have some idea about TDA. Thanks.