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Will it work on high dimensional (but sparse) encoded data? [Question] #14
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Hi there! It would work but in my experience the method is very sensitive to the curse of dimensionality, which is one of its main drawbacks: you will have lots of points mapped to the same coordinates. I'd use PCA preprocessing to get a 50-100 dimensional feature space (depending on the % variance explained...) |
Could the package be parameterized by a distance function? |
Hello, this might be a silly question, but I hope to get your reply. |
Hi Helena!
Let's say my data are represented (X) with a sparse positive integer vector of dimension about 20,000.
And let's say I have a few thousands such data points.
Will ugtm work, or not?
I am asking before I start to invest manpower into using ugtm...
Thanks a lot!
F.
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