{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T10:50:41Z","timestamp":1774263041249,"version":"3.50.1"},"reference-count":3,"publisher":"MIT Press - Journals","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Neural Computation"],"published-print":{"date-parts":[[2001,3,1]]},"abstract":"<jats:p> The proposal of considering nonlinear principal component analysis as a kernel eigenvalue problem has provided an extremely powerful method of extracting nonlinear features for a number of classification and regression applications. Whereas the utilization of Mercer kernels makes the problem of computing principal components in, possibly, infinite-dimensional feature spaces tractable, there are still the attendant numerical problems of diagonalizing large matrices. In this contribution, we propose an expectation-maximization approach for performing kernel principal component analysis and show this to be a computationally efficient method, especially when the number of data points is large. <\/jats:p>","DOI":"10.1162\/089976601300014439","type":"journal-article","created":{"date-parts":[[2002,7,27]],"date-time":"2002-07-27T11:55:01Z","timestamp":1027770901000},"page":"505-510","source":"Crossref","is-referenced-by-count":46,"title":["An Expectation-Maximization Approach to Nonlinear Component Analysis"],"prefix":"10.1162","volume":"13","author":[{"given":"Roman","family":"Rosipal","sequence":"first","affiliation":[{"name":"Computational Intelligence Research Unit, Department of Computing and Information Systems, University of Paisley, Paisley, PA1 2BE, Scotland, U.K."}]},{"given":"Mark","family":"Girolami","sequence":"additional","affiliation":[{"name":"Computational Intelligence Research Unit, Department of Computing and Information Systems, University of Paisley, Paisley, PA1 2BE, Scotland, U.K."}]}],"member":"281","reference":[{"key":"p_3","doi-asserted-by":"publisher","DOI":"10.1162\/089976699300016674"},{"key":"p_5","doi-asserted-by":"publisher","DOI":"10.1162\/089976698300017467"},{"key":"p_6","doi-asserted-by":"publisher","DOI":"10.1111\/1467-9868.00196"}],"container-title":["Neural Computation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mitpressjournals.org\/doi\/pdf\/10.1162\/089976601300014439","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,3,12]],"date-time":"2021-03-12T21:48:35Z","timestamp":1615585715000},"score":1,"resource":{"primary":{"URL":"https:\/\/direct.mit.edu\/neco\/article\/13\/3\/505-510\/6498"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2001,3,1]]},"references-count":3,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2001,3,1]]}},"alternative-id":["10.1162\/089976601300014439"],"URL":"https:\/\/doi.org\/10.1162\/089976601300014439","relation":{},"ISSN":["0899-7667","1530-888X"],"issn-type":[{"value":"0899-7667","type":"print"},{"value":"1530-888X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2001,3,1]]}}}