{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/doubly-stochastic-neighbor-embedding-on","title":"Doubly Stochastic Neighbor Embedding on Spheres","arxiv_id":"1609.01977","date":"2016-09-07","proceeding":null,"authors":["Yao Lu","Jukka Corander","Zhirong Yang"],"abstract":"Stochastic Neighbor Embedding (SNE) methods minimize the divergence between\nthe similarity matrix of a high-dimensional data set and its counterpart from a\nlow-dimensional embedding, leading to widely applied tools for data\nvisualization. Despite their popularity, the current SNE methods experience a\ncrowding problem when the data include highly imbalanced similarities. This\nimplies that the data points with higher total similarity tend to get crowded\naround the display center. To solve this problem, we introduce a fast\nnormalization method and normalize the similarity matrix to be doubly\nstochastic such that all the data points have equal total similarities.\nFurthermore, we show empirically and theoretically that the doubly\nstochasticity constraint often leads to embeddings which are approximately\nspherical. This suggests replacing a flat space with spheres as the embedding\nspace. The spherical embedding eliminates the discrepancy between the center\nand the periphery in visualization, which efficiently resolves the crowding\nproblem. We compared the proposed method (DOSNES) with the state-of-the-art SNE\nmethod on three real-world datasets and the results clearly indicate that our\nmethod is more favorable in terms of visualization quality.","url_abs":"http://arxiv.org/abs/1609.01977v2","url_pdf":"http://arxiv.org/pdf/1609.01977v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"doubly-stochastic-neighbor-embedding-on","repo_url":"https://github.com/yaolubrain/DOSNES","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"data-visualization","task_name":"Data Visualization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1609.01977","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}