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This algorithm is limited by two issues, both pertaining to the explicit kernel map and the matrix sketching algorithms respectively. As a solution, two new scalable DR algorithms called ECM-SKPCA and Euler-SKPCA are proposed. The efficacy of the proposed algorithms as scalable DR algorithms is demonstrated via the task of classification with many publicly available datasets. The results indicate that the proposed algorithms produce more effective features than the previous algorithm for the classification task. Furthermore, ECM-SKPCA is also demonstrated to be much faster than all other algorithms.<\/jats:p>","DOI":"10.3233\/idt-220182","type":"journal-article","created":{"date-parts":[[2023,3,21]],"date-time":"2023-03-21T12:11:39Z","timestamp":1679400699000},"page":"457-470","source":"Crossref","is-referenced-by-count":3,"title":["Dimensionality reduction of large datasets with explicit feature maps"],"prefix":"10.1177","volume":"17","author":[{"given":"Deena P.","family":"Francis","sequence":"first","affiliation":[{"name":"DTU Compute, Technical University of Denmark, Kongens Lyngby, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kumudha","family":"Raimond","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"G. 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