SOURCE-LINKED INTELLIGENCE
On the Abundance of Critical Points of the t-SNE Energy
This paper considers the energy landscape of the t-SNE algorithm. While this algorithm has enjoyed broad adoption, the non-convexity of the associated energy has made it difficult to rigorously understand what the algorithm captures in many settings. In particular, a number of well-known numerical examples, several of which are reproduced in this article, suggest a complicated energy landscape with many local minimizers that do not respect the topology or clustering structure of the underlying data. This work seeks to provide first steps towards a rigorous explanation of these phenomena. Speci
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-03T18:41:29.000Z
First collected: 2026-09-21T04:31:57.454Z. This is not the publication date.