{"url":"/method/geomancer","slug":"geomancer","name":"GEOMANCER","full_name":"Geometric Manifold Component Estimator","full_name_withheld":false,"description_markdown":"**Geomancer** is a nonparametric algorithm for symmetry-based disentangling of data manifolds. It learns a set of subspaces to assign to each point in the dataset, where each subspace is the tangent space of one disentangled submanifold. This means that geomancer can be used to disentangle manifolds for which there may not be a global axis-aligned coordinate system.","description_state":"present","introduced_year":null,"introduced_by":{"title":"Disentangling by Subspace Diffusion","paper":"/paper/disentangling-by-subspace-diffusion","first_author":"David Pfau","n_authors":4,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/disentangling-by-subspace-diffusion"},"source":{"url":"https://arxiv.org/abs/2006.12982v2","title":"Disentangling by Subspace Diffusion","url_on_a_paper_host":true},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"General","area_id":"general","collection":"Manifold Disentangling","url":"/methods/category/manifold-disentangling","pwc_aliases":[]}],"n_papers_tagged":1,"archive_num_papers":1,"papers_newest_first":[{"paper":"/paper/disentangling-by-subspace-diffusion","title":"Disentangling by Subspace Diffusion","date":"2020-06-23","arxiv_id":"2006.12982","n_code_links":1,"syntology":{"ran":6,"of":9,"unverified":3,"pointer_only":0}}],"papers_shown":1,"tasks":[{"task":"/task/metric-learning","name":"Metric Learning","papers":1},{"task":"/task/representation-learning","name":"Representation Learning","papers":1}],"tasks_shown":2,"n_tasks":2,"usage_by_year":[{"year":"2020","papers":1}],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/geomancer"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}