{"id":16735120,"url":"https://github.com/mamba413/eimpute","last_synced_at":"2025-03-15T20:45:09.982Z","repository":{"id":103329183,"uuid":"246566068","full_name":"Mamba413/eimpute","owner":"Mamba413","description":"Efficiently Impute Large Scale Incomplete Matrix","archived":false,"fork":false,"pushed_at":"2024-01-16T16:20:21.000Z","size":4293,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-01-22T09:52:23.006Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"C++","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Mamba413.png","metadata":{"files":{"readme":"README.md","changelog":"NEWS.md","contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2020-03-11T12:34:23.000Z","updated_at":"2022-05-02T07:29:57.000Z","dependencies_parsed_at":"2024-11-21T17:42:25.604Z","dependency_job_id":null,"html_url":"https://github.com/Mamba413/eimpute","commit_stats":{"total_commits":16,"total_committers":2,"mean_commits":8.0,"dds":0.375,"last_synced_commit":"79f4cdbc166cfca73dafe9d10cdcfb79a9efe96b"},"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Mamba413%2Feimpute","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Mamba413%2Feimpute/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Mamba413%2Feimpute/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Mamba413%2Feimpute/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Mamba413","download_url":"https://codeload.github.com/Mamba413/eimpute/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243790944,"owners_count":20348378,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-10-13T00:05:00.740Z","updated_at":"2025-03-15T20:45:09.957Z","avatar_url":"https://github.com/Mamba413.png","language":"C++","funding_links":[],"categories":[],"sub_categories":[],"readme":"# eimpute: Efficiently IMPUTE Large Scale Incomplete Matrix\n\nIntrodution\n----------\nMatrix completion is a procedure for imputing the missing elements in matrices by using the information of observed elements. This procedure can be visualized as:\n\n![](./vignettes/matrixcom.jpg)\n\nMatrix completion has attracted a lot of attention, it is widely applied in:\n- tabular data imputation: recover the missing elements in data table;\n- recommend system: estimate users' potantial preference for items pending purchased;\n- image inpainting: inpaint the missing elements in digit images.\n\n\nSoftware\n----------\nA computationally efficient R package, **eimpute** is developed for matrix completion.\n\n### Installation\nInstall the stable version from CRAN:        \n```R\ninstall.packages(\"eimpute\")\n```\n\n### Advantage\nIn **eimpute**, matrix completion problem is solved by iteratively performing low-rank approximation and data calibration, which enjoy two admirable advantages:\n- unbiased low-rank approximation for incomplete matrix\n- less time consumption via truncated SVD \nMoreover, **eimpute** also supports flexible data standardization.\n\nCompare **eimpute** and **softimpute** in systhesis datasets $X_{m \\times m}$ with $p$ proportion missing observations:\n\n- $m$ is chosen as 1000, 2000, 3000, 4000\n- $p$ is chosen as 0.1, 0.5, 0.9.\n\n![](./vignettes/time3.png)\n![](./vignettes/error3.png)\n\nIn high dimension case, als method in **softimpute** is a little faster than **eimpute** in low proportion of missing observations, as the proportion of missing observations increase, rsvd method in **eimpute** have a better performance than **softimpute** in time cost and test error. Compare with two method in **eimpute*, rsvd method is better than tsvd in time cost.\n\nReferences\n----------\n- Rahul Mazumder, Trevor Hastie and Rob Tibshirani (2010) Spectra Regularization Algorithms for Learning Large Incomplete Matrices, Journal of Machine Learning Research 11 (2010) 2287-2322\n\n- Nathan Halko, Per-Gunnar Martinsson, Joel A. Tropp (2011) Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions, SIAM Review Vol. 53, num. 2, pp. 217-288\n\nBug report\n----------\nSend an email to Zhe Gao at gaozh8@mail2.sysu.edu.cn\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmamba413%2Feimpute","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmamba413%2Feimpute","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmamba413%2Feimpute/lists"}