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Moreover, for the uncentered case, we introduce the error representation, and prove the comparison theorem that the learning error can be bounded by the excess generalization error. Under the condition that the positive eigenvalues of [Formula: see text] are all single, the satisfied error bound [Formula: see text] is deduced. <\/jats:p>","DOI":"10.1142\/s021969132250059x","type":"journal-article","created":{"date-parts":[[2022,11,22]],"date-time":"2022-11-22T16:13:30Z","timestamp":1669133610000},"source":"Crossref","is-referenced-by-count":2,"title":["Learning performance of uncentered kernel-based principal component analysis"],"prefix":"10.1142","volume":"21","author":[{"given":"Xue","family":"Jiang","sequence":"first","affiliation":[{"name":"School of Mathematical Science, University of Jinan, Jinan, Shandong Province 250022, P. R. 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