Graduate Detecting a Torus in a data cloud

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The proposed method for detecting a torus in a data cloud involves uniformly distributing points and creating edges based on a metric value to identify density. The discussion raises questions about the effectiveness of this approach in distinguishing between different topological shapes, such as a coffee mug versus a doughnut. Participants suggest that the topic may be better suited for a computing forum, as it relates to Persistent Homology and Topological Data Analysis. There is a consensus that clearer, more specific questions would yield better answers, as the current formulation is too vague. The thread concludes with a recommendation for the original poster to refine their inquiry and choose the appropriate forum for discussion.
FallenApple
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Would the following method work? I could uniformly distribute points into the data cloud. Of the "darts" that I threw in, create edges between all points with a metric value under a certain amount. The nodes in the resulting graph that have more neighbors would indicate greater density. I could tune the amount of darts and since the connective density would increase in a more non linear fashion for denser regions vs less dense regions, I could tell if there's a cavity or not.

Would this work in practice?

Would it work topologically? That is, I would get the same indication for a coffee mug cloud vs a doughnut? I mean, regardless of the shape, I would end up with two less dense regions.
 
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This is not my area and it seems to be confusing for other mentors, too. In an attempt to get some clarity, could you please provide a considerably more precise sample problem you are trying to solve?

My goal is to get you an answer - good clear questions get answers, fuzzy or overly general questions get more questions.
 
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jim mcnamara said:
This is not my area and it seems to be confusing for other mentors, too. In an attempt to get some clarity, could you please provide a considerably more precise sample problem you are trying to solve?

My goal is to get you an answer - good clear questions get answers, fuzzy or overly general questions get more questions.
In the meantime, best I can think is that this is from Persistent Homology and, more generally, from Topological Data Analysis.
 
WWGD said:
In the meantime, best I can think is that this is from Persistent Homology and, more generally, from Topological Data Analysis.
In this case it would be better placed in the computing forum, isn't it?
 
fresh_42 said:
In this case it would be better placed in the computing forum, isn't it?
Yes, it seems like it would be a better fit there, tho we may make a nonclustered index and include a link to it from here if possible, I would think.
 
Is this essentially the same problem as in image enhancement and image recognition ? These subjects have been extensively researched and there is a large amount of published material available about both the mathematical theory and practical computation methods .
 
Nidum said:
Is this essentially the same problem as in image enhancement and image recognition ? These subjects have been extensively researched and there is a large amount of published material available about both the mathematical theory and practical computation methods .
Not sure, sorry, but sounds right/close. The dart thing suggests a Monte Carlo method for pattern recognition.
 
Since the OP is obviously not well formulated, such that meanwhile everyone guesses what might have been meant, I close this thread. It makes no sense to start a discussion on speculations. The more as it is not clear, if we're right.

@FallenApple If you repost this, please make sure that you're understood and place it in an appropriate forum. If it's computation, then it shouldn't be here.
 

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