Content deleted Content added
m v2.05b - Bot T20 CW#61 - Fix errors for CW project (Reference before punctuation) |
m Open access bot: doi updated in citation with #oabot. |
||
Line 51:
== Output ==
While t-SNE plots often seem to display [[cluster analysis|clusters]], the visual clusters can be strongly influenced by the chosen parameterization and so a good understanding of the parameters for t-SNE is needed. Such "clusters" can be shown to even appear in structured data with no clear clustering,<ref>{{Cite web |title=K-means clustering on the output of t-SNE |url=https://stats.stackexchange.com/a/264647 |access-date=2018-04-16 |website=Cross Validated}}</ref> and so may be false findings. Similarly, the size of clusters produced by t-SNE is not informative, and neither is the distance between clusters.<ref>{{Cite journal |last=Wattenberg |first=Martin |last2=Viégas |first2=Fernanda |last3=Johnson |first3=Ian |date=2016-10-13 |title=How to Use t-SNE Effectively |url=http://distill.pub/2016/misread-tsne |journal=Distill |language=en |volume=1 |issue=10 |pages=e2 |doi=10.23915/distill.00002 |issn=2476-0757|doi-access=free }}</ref> Thus, interactive exploration may be needed to choose parameters and validate results.<ref>{{Cite journal |last1=Pezzotti |first1=Nicola |last2=Lelieveldt |first2=Boudewijn P. F. |last3=Maaten |first3=Laurens van der |last4=Hollt |first4=Thomas |last5=Eisemann |first5=Elmar |last6=Vilanova |first6=Anna |date=2017-07-01 |title=Approximated and User Steerable tSNE for Progressive Visual Analytics |journal=IEEE Transactions on Visualization and Computer Graphics |language=en-US |volume=23 |issue=7 |pages=1739–1752 |arxiv=1512.01655 |doi=10.1109/tvcg.2016.2570755 |issn=1077-2626 |pmid=28113434 |s2cid=353336}}</ref><ref>{{cite journal |last1=Wattenberg |first1=Martin |last2=Viégas |first2=Fernanda |last3=Johnson |first3=Ian |date=2016-10-13 |title=How to Use t-SNE Effectively |url=https://distill.pub/2016/misread-tsne/ |journal=Distill |language=en |volume=1 |issue=10 |doi=10.23915/distill.00002 |access-date=4 December 2017 |doi-access=free}}</ref> It has been shown that t-SNE can often recover well-separated clusters, and with special parameter choices, approximates a simple form of [[spectral clustering]].<ref>{{cite arXiv |eprint=1706.02582 |class=cs.LG |first1=George C. |last1=Linderman |first2=Stefan |last2=Steinerberger |title=Clustering with t-SNE, provably |date=2017-06-08}}</ref>
== Software ==
| |||