{"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/convex-formulations-for-fair-principal","title":"Convex Formulations for Fair Principal Component Analysis","arxiv_id":"1802.03765","date":"2018-02-11","proceeding":null,"authors":["Matt Olfat","Anil Aswani"],"abstract":"Though there is a growing body of literature on fairness for supervised\nlearning, the problem of incorporating fairness into unsupervised learning has\nbeen less well-studied. This paper studies fairness in the context of principal\ncomponent analysis (PCA). We first present a definition of fairness for\ndimensionality reduction, and our definition can be interpreted as saying that\na reduction is fair if information about a protected class (e.g., race or\ngender) cannot be inferred from the dimensionality-reduced data points. Next,\nwe develop convex optimization formulations that can improve the fairness (with\nrespect to our definition) of PCA and kernel PCA. These formulations are\nsemidefinite programs (SDP's), and we demonstrate the effectiveness of our\nformulations using several datasets. We conclude by showing how our approach\ncan be used to perform a fair (with respect to age) clustering of health data\nthat may be used to set health insurance rates.","url_abs":"http://arxiv.org/abs/1802.03765v3","url_pdf":"http://arxiv.org/pdf/1802.03765v3.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":"convex-formulations-for-fair-principal","repo_url":"https://github.com/molfat66/FairML","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"convex-formulations-for-fair-principal","repo_url":"https://github.com/amazon-science/fair-pca","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"dimensionality-reduction","task_name":"Dimensionality Reduction"},{"task_slug":"fairness","task_name":"Fairness"}],"methods":[{"method_slug":"pca","method_name":"PCA"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1802.03765","atlas_url":"https://app.syntology.ai/?focus=1802.03765","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1802.03765"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/amazon-science/fair-pca","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/molfat66/FairML","reach":null}],"summary":{"ran_draft_wrong":1},"by_repo_kind":{"official":{"samples":1,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":1,"samples":[{"code_sha256_prefix":"26fa691b2924bea5","entry":"getSymMat","repo":"molfat66/FairML","repo_kind":"official","path":"mosPCAMult.py","file_url":"https://github.com/molfat66/FairML/blob/HEAD/mosPCAMult.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"26fa691b2924bea5"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}