{"id":9552,"date":"2026-07-27T12:30:27","date_gmt":"2026-07-27T10:30:27","guid":{"rendered":"https:\/\/www.kubicek.ai\/?post_type=lexicon&#038;p=9552"},"modified":"2026-07-27T13:25:49","modified_gmt":"2026-07-27T11:25:49","slug":"t-sne-and-umap","status":"publish","type":"lexicon","link":"https:\/\/www.kubicek.ai\/en\/lexicon\/t-sne-and-umap\/","title":{"rendered":"t-SNE and UMAP"},"content":{"rendered":"<p class=\"wp-block-paragraph\"><strong>t-SNE and UMAP<\/strong> are non-linear dimensionality reduction methods intended primarily for visualising high-dimensional data in two or three dimensions. Unlike PCA, which tries to preserve global variance, they concentrate on preserving local structure \u2013 on keeping points that were close in the original space close on the resulting map. t-SNE converts distances into neighbourhood probabilities and minimises the Kullback-Leibler divergence between the distributions in the original and target spaces, using a heavy-tailed Student&#8217;s t-distribution in the target space so that clusters push apart. UMAP builds on Riemannian geometry and the topology of the neighbourhood graph; it is orders of magnitude faster, scales to millions of points and preserves more global structure as well. A crucial caveat for interpretation: cluster sizes and the distances between them have no reliable meaning, the result depends strongly on the perplexity or number-of-neighbours setting, and the methods are not well suited to projecting new points. They are thus an exploratory tool rather than an input to another model \u2013 commonly used to display clusters of embeddings.<\/p>\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n<p class=\"wp-block-paragraph\">Imagine having to flatten a globe onto a sheet of paper. It cannot be done without distortion \u2013 something has to be sacrificed. t-SNE and UMAP choose to sacrifice large distances and preserve neighbourhoods: they guarantee that cities next to each other stay next to each other on the map, but they care nothing about how far London is from Tokyo. On such a map you can reliably say &#8220;here is a clear cluster of countries&#8221;, but you must never read off that one cluster is twice as far away as another. It is a tool for the eye, not for the ruler.<\/p>\n","protected":false},"featured_media":0,"template":"","class_list":["post-9552","lexicon","type-lexicon","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.kubicek.ai\/en\/wp-json\/wp\/v2\/lexicon\/9552","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.kubicek.ai\/en\/wp-json\/wp\/v2\/lexicon"}],"about":[{"href":"https:\/\/www.kubicek.ai\/en\/wp-json\/wp\/v2\/types\/lexicon"}],"wp:attachment":[{"href":"https:\/\/www.kubicek.ai\/en\/wp-json\/wp\/v2\/media?parent=9552"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}