{"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/robust-and-scalable-learning-of-complex","title":"Robust And Scalable Learning Of Complex Dataset Topologies Via Elpigraph","arxiv_id":"1804.07580","date":"2018-04-20","proceeding":null,"authors":["Luca Albergante","Evgeny M. Mirkes","Huidong Chen","Alexis Martin","Louis Faure","Emmanuel Barillot","Luca Pinello","Alexander N. Gorban","Andrei Zinovyev"],"abstract":"Large datasets represented by multidimensional data point clouds often\npossess non-trivial distributions with branching trajectories and excluded\nregions, with the recent single-cell transcriptomic studies of developing\nembryo being notable examples. Reducing the complexity and producing compact\nand interpretable representations of such data remains a challenging task. Most\nof the existing computational methods are based on exploring the local data\npoint neighbourhood relations, a step that can perform poorly in the case of\nmultidimensional and noisy data. Here we present ElPiGraph, a scalable and\nrobust method for approximation of datasets with complex structures which does\nnot require computing the complete data distance matrix or the data point\nneighbourhood graph. This method is able to withstand high levels of noise and\nis capable of approximating complex topologies via principal graph ensembles\nthat can be combined into a consensus principal graph. ElPiGraph deals\nefficiently with large and complex datasets in various fields from biology,\nwhere it can be used to infer gene dynamics from single-cell RNA-Seq, to\nastronomy, where it can be used to explore complex structures in the\ndistribution of galaxies.","url_abs":"http://arxiv.org/abs/1804.07580v2","url_pdf":"http://arxiv.org/pdf/1804.07580v2.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":"robust-and-scalable-learning-of-complex","repo_url":"https://github.com/j-bac/ElPiGraph_Python","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"robust-and-scalable-learning-of-complex","repo_url":"https://github.com/j-bac/elpigraph-python","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"astronomy","task_name":"Astronomy"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}