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To deal with this, the Nonnegative Tensor Factorization (NTF) based methods have been widely used. However existing factorization algorithms are not suitable to process in all three conditions of size, density, and rank of the tensor. Consequently, their applicability becomes limited. In this article, we propose a novel fast and efficient NTF algorithm using the element selection approach. We calculate the element importance using Lipschitz continuity and propose a saturation point-based element selection method that chooses a set of elements column-wise for updating to solve the optimization problem. Empirical analysis reveals that the proposed algorithm is scalable in terms of tensor size, density, and rank in comparison to the relevant state-of-the-art algorithms.<\/jats:p>","DOI":"10.1145\/3385654","type":"journal-article","created":{"date-parts":[[2020,5,30]],"date-time":"2020-05-30T12:25:16Z","timestamp":1590841516000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["Efficient Nonnegative Tensor Factorization via Saturating Coordinate Descent"],"prefix":"10.1145","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8821-6003","authenticated-orcid":false,"given":"Thirunavukarasu","family":"Balasubramaniam","sequence":"first","affiliation":[{"name":"Queensland University of Technology, Brisbane, Queensland, Australia"}]},{"given":"Richi","family":"Nayak","sequence":"additional","affiliation":[{"name":"Queensland University of Technology, Brisbane, Queensland, Australia"}]},{"given":"Chau","family":"Yuen","sequence":"additional","affiliation":[{"name":"Singapore University of Technology and Design, Singapore"}]}],"member":"320","published-online":{"date-parts":[[2020,5,30]]},"reference":[{"volume-title":"Dunlavy","year":"2011","author":"Acar Evrim","key":"e_1_2_1_1_1"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2005.99"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1137\/060676489"},{"volume-title":"Daniel Dunlavy, Robert Bassett, Casey J. 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