Just that the material featured most prominently on the site might be too imprecise for mathematicians and scientists with significant technical expertise. It would be nice if there was a convenient link I could pass along to informed, but highly skeptical, people with a genuine interest in understanding the platform on a deeper level.
The key with Ayasdi's work is that they manage to layer the TDA with different ML filters, which does stunningly well for the datasets they like talking about.
I'd love to see a success story of this type of analysis outside of their canned examples. I keep seeing them use the same datasets over and over again without any real benchmarks to state of the art. Its amazing how a data product is being sold without any empirical studies or benchmark datasets.
Ayasdi seems successful to me in that it has a lot of flash and their results make intuitive sense, but I don't understand how a practicing data scientist would use this.
The key paragraph "The projection is visualized as ... pictured as below." is very ambiguous, and completely missing the explanation of how the data was split into red, blue, and indigo clusters.
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[ 3.5 ms ] story [ 36.0 ms ] threadI also second the notion to read papers from the Carlsson lab, particularly one of the more application-oriented papers such as this one: http://www.nature.com/srep/2013/130207/srep01236/pdf/srep012...
Ayasdi seems successful to me in that it has a lot of flash and their results make intuitive sense, but I don't understand how a practicing data scientist would use this.
I'm working on a project where I'm using similar methods, but not from Ayasdi, to study cyclic phenomena in high-dimensional data.