{"type":"ScholarlyArticle","title":"Single-Pass PCA of Large High-Dimensional Data","authors":[{"name":"Wenjian Yu","slug":"wenjian-yu","given":"Wenjian","family":"Yu","affiliation":""},{"name":"Yu Gu","slug":"yu-gu","given":"Yu","family":"Gu","affiliation":""},{"name":"Jian Li","slug":"jian-li","given":"Jian","family":"Li","affiliation":""},{"name":"Shenghua Liu","slug":"shenghua-liu","given":"Shenghua","family":"Liu","affiliation":""},{"name":"Yaohang Li","slug":"yaohang-li","given":"Yaohang","family":"Li","affiliation":""}],"abstract":"Principal component analysis (PCA) is a fundamental dimension reduction tool in statistics and machine learning. For large and high-dimensional data, computing the PCA (i.e., the top singular vectors of the data matrix) becomes a challenging task. In this work, a single-pass randomized algorithm is proposed to compute PCA with only one pass over the data. It is suitable for processing extremely large and high-dimensional data stored in slow memory (hard disk) or the data generated in a streaming fashion. Experiments with synthetic and real data validate the algorithm's accuracy, which has orders of magnitude smaller error than an existing single-pass algorithm. For a set of high-dimensional data stored as a 150 GB file, the algorithm is able to compute the first 50 principal components in just 24 minutes on a typical 24-core computer, with less than 1 GB memory cost.","doi":"10.24963/ijcai.2017/468","url":"https://ijcai.org/proceedings/2017/468","pdf":"https://ijcai.org/proceedings/2017/0468.pdf","markdown":"https://ijcai.org/proceedings/2017/468.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/468","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra, IIIA-CSIC","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Machine Learning S-Z","track":"Main track","number":468,"pages":{"first":3350,"last":3356},"keywords":["Machine Learning: Data Mining","Machine Learning: Feature Selection/Construction","Machine Learning: Time-series/Data Streams"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}