Assessing and improving reliability of neighbor embedding methods: a map-continuity perspective · HackerPapers discussionshttps://hackerpaper.kaipfister.de/p/2410.16608/Collected HN comments and submissions linking this paper.enMon, 02 Nov 2026 11:16:22 GMTAssessing and improving reliability of neighbor embedding methods: a map-continuity perspective — mentioned by patconhttps://news.ycombinator.com/item?id=45942497urn:hackerpapers:mention:2410.16608:45942497Sun, 16 Nov 2025 03:23:46 GMT<p>This reminds me the talk by Leland McInness (employed by a branch of Canadian NSA and creator of UMAP) on rethinking dimensional reduction: Rethinking Unsupervised Learning https://youtu.be/v_jcu7rUqqE He boiled all dimensionality reduction down to two approaches: matrix decomposition and k-nearest neighbors. I&#x27;m seeing the newest contenders for top algorithms leaning harder into the KNN dynamics, and less into the more complex maths of UMAP. (PaCMAP[1] and LocalMAP[2], which run via an attractive-repulsive force simulation based on high dimensional KNN graph) Based on the end of his talk…</p><p><a href="https://hackerpaper.kaipfister.de/p/2410.16608/">Paper and discussion history</a>. A mention is not an endorsement.</p>stat.ME