{"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/generative-locally-linear-embedding-a-module","title":"Generative locally linear embedding: A module for manifold unfolding and visualization","arxiv_id":null,"date":"2021-07-06","proceeding":"Software Impacts 2021 7","authors":["Benyamin Ghojogh","Ali Ghodsi","Fakhri Karray","Mark Crowley"],"abstract":"Data often have nonlinear patterns in machine learning. One can unfold the nonlinear manifold of a dataset for low-dimensional visualization and feature extraction. Locally Linear Embedding (LLE) is a nonlinear spectral method for dimensionality reduction and manifold unfolding. It embeds data using the same linear reconstruction weights as in the input space. In this paper, we propose an open source module which not only implements LLE, but also includes implementations of two generative LLE algorithms whose linear reconstruction phases are stochastic. Using this module, one can generate as many manifold unfoldings as desired for data visualization or feature extraction.","url_abs":"https://www.sciencedirect.com/science/article/pii/S2665963821000403","url_pdf":"https://reader.elsevier.com/reader/sd/pii/S2665963821000403?token=698ED2EB9C0621DEAFDC0F07676C0090EE1662DD4772437E7045BF329CA4E8E63BFA9B178E8F606546A994BA58E4D591&originRegion=us-east-1&originCreation=20210726222653","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":"generative-locally-linear-embedding-a-module","repo_url":"https://github.com/bghojogh/Generative-LLE","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"data-visualization","task_name":"Data Visualization"},{"task_slug":"dimensionality-reduction","task_name":"Dimensionality Reduction"},{"task_slug":"variational-inference","task_name":"Variational Inference"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}