{"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/lazy-stochastic-principal-component-analysis","title":"Lazy stochastic principal component analysis","arxiv_id":"1709.07175","date":"2017-09-21","proceeding":null,"authors":["Michael Wojnowicz","Dinh Nguyen","Li Li","Xuan Zhao"],"abstract":"Stochastic principal component analysis (SPCA) has become a popular\ndimensionality reduction strategy for large, high-dimensional datasets. We\nderive a simplified algorithm, called Lazy SPCA, which has reduced\ncomputational complexity and is better suited for large-scale distributed\ncomputation. We prove that SPCA and Lazy SPCA find the same approximations to\nthe principal subspace, and that the pairwise distances between samples in the\nlower-dimensional space is invariant to whether SPCA is executed lazily or not.\nEmpirical studies find downstream predictive performance to be identical for\nboth methods, and superior to random projections, across a range of predictive\nmodels (linear regression, logistic lasso, and random forests). In our largest\nexperiment with 4.6 million samples, Lazy SPCA reduced 43.7 hours of\ncomputation to 9.9 hours. Overall, Lazy SPCA relies exclusively on matrix\nmultiplications, besides an operation on a small square matrix whose size\ndepends only on the target dimensionality.","url_abs":"http://arxiv.org/abs/1709.07175v1","url_pdf":"http://arxiv.org/pdf/1709.07175v1.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":"lazy-stochastic-principal-component-analysis","repo_url":"https://github.com/CylanceSPEAR/lazy-stochastic-principal-component-analysis","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"dimensionality-reduction","task_name":"Dimensionality Reduction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}