BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Lorenzo Rosasco (Universitá di Genova\, IT - MIT\, US) DTSTART:20200504T120000Z DTEND:20200504T124500Z DTSTAMP:20260423T024513Z UID:OWMADS/3 DESCRIPTION:Title: E fficient kernel-PCA by Nyström sampling\nby Lorenzo Rosasco (Universi tá di Genova\, IT - MIT\, US) as part of One World seminar: Mathematical Methods for Arbitrary Data Sources (MADS)\n\n\nAbstract\nIn this talk\, we discuss and study a Nyström based approach to efficient large scale kern el principal component analysis (PCA). The latter is a natural nonlinear e xtension of classical PCA based on considering a nonlinear feature map or the corresponding kernel. Like other kernel approaches\, kernel PCA enjoys good mathematical and statistical properties but\, numerically\, it scale s poorly with the sample size. Our analysis shows that Nyström sampling g reatly improves computational efficiency without incurring any loss of sta tistical accuracy. While similar effects have been observed in supervised learning\, this is the first such result for PCA. Our theoretical findings \, which are also illustrated by numerical results\, are based on a combin ation of analytic and concentration of measure techniques. Our study is mo re broadly motivated by the question of understanding the interplay betwee n statistical and computational requirements for learning.\n LOCATION:https://researchseminars.org/talk/OWMADS/3/ END:VEVENT END:VCALENDAR