{"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/the-price-of-fair-pca-one-extra-dimension","title":"The Price of Fair PCA: One Extra Dimension","arxiv_id":"1811.00103","date":"2018-10-31","proceeding":"NeurIPS 2018 12","authors":["Samira Samadi","Uthaipon Tantipongpipat","Jamie Morgenstern","Mohit Singh","Santosh Vempala"],"abstract":"We investigate whether the standard dimensionality reduction technique of PCA\ninadvertently produces data representations with different fidelity for two\ndifferent populations. We show on several real-world data sets, PCA has higher\nreconstruction error on population A than on B (for example, women versus men\nor lower- versus higher-educated individuals). This can happen even when the\ndata set has a similar number of samples from A and B. This motivates our study\nof dimensionality reduction techniques which maintain similar fidelity for A\nand B. We define the notion of Fair PCA and give a polynomial-time algorithm\nfor finding a low dimensional representation of the data which is\nnearly-optimal with respect to this measure. Finally, we show on real-world\ndata sets that our algorithm can be used to efficiently generate a fair low\ndimensional representation of the data.","url_abs":"http://arxiv.org/abs/1811.00103v1","url_pdf":"http://arxiv.org/pdf/1811.00103v1.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":"the-price-of-fair-pca-one-extra-dimension","repo_url":"https://github.com/samirasamadi/Fair-PCA","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"dimensionality-reduction","task_name":"Dimensionality Reduction"}],"methods":[{"method_slug":"pca","method_name":"PCA"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1811.00103","atlas_url":"https://app.syntology.ai/?focus=1811.00103","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}