{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T13:16:35Z","timestamp":1753881395001,"version":"3.41.2"},"reference-count":38,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2019,2,11]],"date-time":"2019-02-11T00:00:00Z","timestamp":1549843200000},"content-version":"vor","delay-in-days":41,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61773244","61373079","61572344"],"award-info":[{"award-number":["61773244","61373079","61572344"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2019,1]]},"abstract":"<jats:p>As a powerful nonlinear feature extractor, kernel principal component analysis (KPCA) has been widely adopted in many machine learning applications. However, KPCA is usually performed in a batch mode, leading to some potential problems when handling massive or online datasets. To overcome this drawback of KPCA, in this paper, we propose a two\u2010phase incremental KPCA (TP\u2010IKPCA) algorithm which can incorporate data into KPCA in an incremental fashion. In the first phase, an incremental algorithm is developed to explicitly express the data in the kernel space. In the second phase, we extend an incremental principal component analysis (IPCA) to estimate the kernel principal components. Extensive experimental results on both synthesized and real datasets showed that the proposed TP\u2010IKPCA produces similar principal components as conventional batch\u2010based KPCA but is computationally faster than KPCA and its several incremental variants. Therefore, our algorithm can be applied to massive or online datasets where the batch method is not available.<\/jats:p>","DOI":"10.1155\/2019\/5937274","type":"journal-article","created":{"date-parts":[[2019,2,11]],"date-time":"2019-02-11T23:36:34Z","timestamp":1549928194000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Two\u2010Phase Incremental Kernel PCA for Learning Massive or Online Datasets"],"prefix":"10.1155","volume":"2019","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2410-435X","authenticated-orcid":false,"given":"Feng","family":"Zhao","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5595-6673","authenticated-orcid":false,"given":"Islem","family":"Rekik","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6249-4996","authenticated-orcid":false,"given":"Seong-Whan","family":"Lee","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3960-6902","authenticated-orcid":false,"given":"Jing","family":"Liu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0147-7578","authenticated-orcid":false,"given":"Junying","family":"Zhang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7934-5698","authenticated-orcid":false,"given":"Dinggang","family":"Shen","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2019,2,11]]},"reference":[{"key":"e_1_2_11_1_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-1904-8"},{"key":"e_1_2_11_2_2","doi-asserted-by":"publisher","DOI":"10.1162\/089976698300017467"},{"key":"e_1_2_11_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.05.047"},{"key":"e_1_2_11_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.isatra.2017.09.015"},{"key":"e_1_2_11_5_2","doi-asserted-by":"publisher","DOI":"10.1049\/iet-com.2017.0212"},{"key":"e_1_2_11_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2005.181"},{"key":"e_1_2_11_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2007.08.001"},{"key":"e_1_2_11_8_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10479-015-1889-2"},{"key":"e_1_2_11_9_2","doi-asserted-by":"publisher","DOI":"10.1162\/089976601300014439"},{"key":"e_1_2_11_10_2","first-page":"1893","article-title":"Fast iterative kernel principal component analysis","volume":"8","author":"Simon G.","year":"2007","journal-title":"Journal of Machine Learning Research (JMLR)"},{"key":"e_1_2_11_11_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11063-004-0036-x"},{"key":"e_1_2_11_12_2","doi-asserted-by":"crossref","unstructured":"Vojt\u011bchF.andV\u00e1clavH. Greedy algorithm for a training set reduction in the kernel methods 2756 Proceedings of the 10th International Conference on Computer Analysis of Image and Patterns August 2003 Groningen Netherlands Springer 426\u2013433 Lecture Notes in Comput. Sci. https:\/\/doi.org\/10.1007\/978-3-540-45179-2_53 MR2120748.","DOI":"10.1007\/978-3-540-45179-2_53"},{"key":"e_1_2_11_13_2","unstructured":"VojtechF. Optimization Algorithms for Kernel Methods [Ph.D. Dissertation] 2005 Center for Machine Perception Czech Technical University Prague Czech Republic."},{"key":"e_1_2_11_14_2","doi-asserted-by":"crossref","unstructured":"ChinT.andSuterD. Incremental kernel PCA for efficient non-linear feature extraction Proceedings of the 17th British Machine Vision Conference September 2006 Edinburgh Scotland 4\u20137 https:\/\/doi.org\/10.5244\/C.20.96.","DOI":"10.5244\/C.20.96"},{"key":"e_1_2_11_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2007.896668"},{"key":"e_1_2_11_16_2","doi-asserted-by":"crossref","unstructured":"KimB. J.andKimI. K. Incremental nonlinear PCA for classification 3202 Proceedings of the European Conference on Knowledge Discovery in Databases (PKDD) 2004 Springer 291\u2013300 Lecture Notes in Computer Science https:\/\/doi.org\/10.1007\/978-3-540-30116-5_28.","DOI":"10.1007\/978-3-540-30116-5_28"},{"key":"e_1_2_11_17_2","doi-asserted-by":"crossref","unstructured":"KimB.-J. Active visual learning and recognition using incremental kernel PCA 3809 Proceedings of the 18th Australian Joint Conference on Advances in Artificial Intelligence AI\u201905 2005 Springer 585\u2013592 Lecture Notes in Comput. Sci. https:\/\/doi.org\/10.1007\/11589990_61 MR2236994.","DOI":"10.1007\/11589990_61"},{"key":"e_1_2_11_18_2","doi-asserted-by":"crossref","unstructured":"HallP. M. MarshallD. andMartinR. R. Incremental eigenanalysis for classification Proceedings of the British Machine Vision Conference 1998 286\u2013295 https:\/\/doi.org\/10.5244\/C.12.29.","DOI":"10.5244\/C.12.29"},{"key":"e_1_2_11_19_2","doi-asserted-by":"crossref","unstructured":"KimuraS. OzawaS. andAbeS. Incremental Kernel PCA for online learning of feature space 1 Proceedings of the 2005 International Conference on Computational Intelligence for Modelling Control and Automation November 2005 Vienna Austria 595\u2013600 2-s2.0-33847230234.","DOI":"10.1109\/CIMCA.2005.1631328"},{"key":"e_1_2_11_20_2","doi-asserted-by":"crossref","unstructured":"TakeuchiY. OzawaS. andAbeS. An efficient incremental kernel principal component analysis for online feature selection Proceedings of the 2007 International Joint Conference on Neural Networks August 2007 Orlando FL USA 2346\u20132351 https:\/\/doi.org\/10.1109\/IJCNN.2007.4371325.","DOI":"10.1109\/IJCNN.2007.4371325"},{"key":"e_1_2_11_21_2","doi-asserted-by":"crossref","unstructured":"SeiichiO. TakeuchiY. andShigeoA. A fast incremental kernel principal component analysis for online feature extraction 6230 Proceedings of the Pacific Rim International Conference on Trends in Artificial Intelligence 2010 Springer 487\u2013497 Lecture Notes in Computer Science https:\/\/doi.org\/10.1007\/978-3-642-15246-7_45.","DOI":"10.1007\/978-3-642-15246-7_45"},{"key":"e_1_2_11_22_2","doi-asserted-by":"crossref","unstructured":"TakaomiT.andSeiichiO. A fast incremental kernel principal component analysis for learning stream of data chunks Proceedings of the 2011 International Joint Conference on Neural Networks (IJCNN 2011 - San Jose) July 2011 San Jose CA USA 2881\u20132888 https:\/\/doi.org\/10.1109\/IJCNN.2011.6033599.","DOI":"10.1109\/IJCNN.2011.6033599"},{"key":"e_1_2_11_23_2","doi-asserted-by":"crossref","unstructured":"JosephA. A.andOzawaS. A fast incremental kernel principal component analysis for data streams Proceedings of the 2014 International Joint Conference on Neural Networks (IJCNN) July 2014 Beijing China https:\/\/doi.org\/10.1109\/IJCNN.2014.6889940.","DOI":"10.1109\/IJCNN.2014.6889940"},{"key":"e_1_2_11_24_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12530-015-9131-7"},{"key":"e_1_2_11_25_2","unstructured":"FredrikH.andPaulN. Incremental kernel PCA and the Nystr\u00f6m method 2018 https:\/\/arxiv.org\/abs\/1802.00043."},{"key":"e_1_2_11_26_2","doi-asserted-by":"crossref","unstructured":"BaudatG.andAnouarF. Kernel-based methods and function approximation Proceedings of the International Joint Conference on Neural Networks (IJCNN\u203201) July 2001 Washington DC USA 1244\u20131249 2-s2.0-0034861805.","DOI":"10.1109\/IJCNN.2001.939539"},{"key":"e_1_2_11_27_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCB.2006.870645"},{"key":"e_1_2_11_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2003.1217609"},{"key":"e_1_2_11_29_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0167-9473(99)00063-8"},{"key":"e_1_2_11_30_2","doi-asserted-by":"publisher","DOI":"10.1016\/0893-6080(89)90044-0"},{"key":"e_1_2_11_31_2","doi-asserted-by":"publisher","DOI":"10.1007\/BF00275687"},{"key":"e_1_2_11_32_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2003.11.010"},{"key":"e_1_2_11_33_2","doi-asserted-by":"crossref","unstructured":"ArtacM. JoganM. andLeonardisA. Incremental PCA for on-line visual learning and recognition Proceedings of the 16th International Conference on Pattern Recognition 2002 Quebec City Canada 781\u2013784 https:\/\/doi.org\/10.1109\/ICPR.2002.1048133.","DOI":"10.1109\/ICPR.2002.1048133"},{"key":"e_1_2_11_34_2","doi-asserted-by":"crossref","unstructured":"SeiichiO. ShaoningP. andNikolaK. A modified incremental principal component analysis for on-line learning of feature space and classifier Proceedings of the 8th Pacific Rim International Conference on Artificial Intelligence 2004 Auckland New Zealand 231\u2013240.","DOI":"10.1007\/978-3-540-28633-2_26"},{"volume-title":"Matrix Analysis","year":"2013","author":"Horn R. A.","key":"e_1_2_11_35_2"},{"key":"e_1_2_11_36_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00006-014-0496-7"},{"key":"e_1_2_11_37_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.amc.2009.01.081"},{"key":"e_1_2_11_38_2","first-page":"524","volume-title":"Advances in Neural Information Processing System","author":"Mike S. B.","year":"1999"}],"container-title":["Complexity"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/complexity\/2019\/5937274.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/complexity\/2019\/5937274.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2019\/5937274","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T11:20:41Z","timestamp":1723029641000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2019\/5937274"}},"subtitle":[],"editor":[{"given":"Jose","family":"Garcia-Rodriguez","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2019,1]]},"references-count":38,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2019,1]]}},"alternative-id":["10.1155\/2019\/5937274"],"URL":"https:\/\/doi.org\/10.1155\/2019\/5937274","archive":["Portico"],"relation":{},"ISSN":["1076-2787","1099-0526"],"issn-type":[{"type":"print","value":"1076-2787"},{"type":"electronic","value":"1099-0526"}],"subject":[],"published":{"date-parts":[[2019,1]]},"assertion":[{"value":"2018-10-02","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2019-01-08","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2019-02-11","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"5937274"}}