Topological methods for unsupervised learning problems [video]
1–10 of 14 posts
Re: Topological methods for unsupervised learning problems [video]
#2Uniform Manifold Approximation and Projection
Re: Topological methods for unsupervised learning problems [video]
#3I’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 actually unlock. Seems like there is also opportunity to draw inspiration from the applied category theory crew (Spivak, Fong etc) to use some CT tools to approach data science from a fresh perspective.
Some of the research coming out is interesting, but as a practitioner I’m more interested in seeing how TDA can add differentiated value in a business context. Interested to hear where people see the field moving next.
Re: Topological methods for unsupervised learning problems [video]
#4Uniform Manifold Approximation and Projection https://arxiv.org/abs/1802.03426 https://github.com/lmcinnes/umap
Context: the speaker in this talk (Leland McInnes) is one of the authors of the UMAP paper and corresponding code repo.
Re: Topological methods for unsupervised learning problems [video]
#5I’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…
>>UMAP has already demonstrated its efficacy as a tool in any data scientist’s belt.
Any chance you have some references? All their examples in the UMAP paper and in this talk look very toy-like.
Re: Topological methods for unsupervised learning problems [video]
#6I’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…
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Re: Topological methods for unsupervised learning problems [video]
#7I’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…
>>UMAP has already demonstrated its efficacy as a tool in any data scientist’s belt. Any chance you have some references? All their examples in the UMAP paper and in this talk look very toy-like.
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Re: Topological methods for unsupervised learning problems [video]
#8Five minutes reading https://johnhw.github.io/umap_primes/index.md.html
Without using any category, topology of sheaf theory, this is what I believe is in this paper:
(1) the prior hypothesis is that data points in R^n are a sample from a uniform distribution in a Riemann space.
(2) Try to define a Riemann metric such that the number of sample points in any ball B is propotional to the volume of B.
(3) Since (2) doesn't define a global Riemann metric, they define a fuzzy membership relation. I suppose the role of the fuzzy tool is that local distance information is weighted according to the variance of the local distance estimations.
Disclaimer, I could be completely wrong.
Re: Topological methods for unsupervised learning problems [video]
#9I’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 working on this but I don't know what I'm not seeing. The "big idea" is taking local data and seeing if it patches together to a global coherent whole or not. Sometimes it doesn't (old Russian example: arbitrage in currency exchange networks). Or sometimes it's about using interpolation to fill in missing data, if you know that it's something for which there is a global function (temperatures across ocean surfaces), or providing a probability distribution for the missing parts. Category theory has something to offer here as well.
Anyone know more about any of these things?
Re: Topological methods for unsupervised learning problems [video]
#10I’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…
"The big idea is taking local data and seeing if it patches together."
I wonder if those new techniques are different from the usually used like: weighted averages of random forest, cross entropy of density estimation, minimization of variance between local estimations and the like.