Comments (3)
I agree you would want to get the same results in both cases (as long as you use the same seed using set.seed). The reason I have not been able to make this happen (I think) is that tiny changes in the distance calculations (compare, for instance, the dist() objects for the two feature orderings, they are not exactly the same) will affect the tsne optimisation and this lead to a different solution. If you have a different explanation of the effect, please let me know. Hope this helps!
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Thanks for you answer!
I try again with your suggestions (i.e. use the same seed using set.seed), but unfortunately, the results are still different, and the good news is that the difference is not big.
And i agree your explanation may be the reasonable reason about the dist() question.
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Just to be clear: the same explanation holds for the matrix input. In that case the distances are calculated in the C++ code, but the same reasoning holds.
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Related Issues (20)
- Variance needed for both axes HOT 1
- missing datapoint for big data? HOT 3
- Using kNN matrix directly in Barnes-Hut HOT 10
- Wish: prevent deteriorating between iterations HOT 2
- Rtsne error: "Remove duplicates before running TSNE." HOT 3
- why check_duplicates? HOT 9
- constraint to dims now %in% 1,2,3 intended? HOT 9
- Failed to load the distance matrix from aligned DNA sequences HOT 1
- Rtsne not consistant between Linux and Windows HOT 1
- Is num_threads = 1.0 really using all the threads? HOT 6
- itercost increment HOT 3
- Single-threaded on Windows, multi-threaded on Linux HOT 3
- Add option for density-preserving t-SNE HOT 2
- Error in library Rtsne HOT 3
- tsne versus som HOT 1
- Rtsne on large dataset HOT 2
- Multiple tsne plots comparable HOT 5
- Failed to install the Rtsne package on the MacOS HOT 5
- Error in complete.cases(hto_dist_mtx) : long vectors not supported yet: complete_cases.c:192 HOT 3
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