{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T11:24:35Z","timestamp":1742988275928,"version":"3.40.3"},"publisher-location":"Cham","reference-count":13,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031162091"},{"type":"electronic","value":"9783031162107"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-16210-7_27","type":"book-chapter","created":{"date-parts":[[2022,9,20]],"date-time":"2022-09-20T23:03:09Z","timestamp":1663714989000},"page":"330-336","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Approximating Sparse Semi-nonnegative Matrix Factorization for\u00a0X-Ray Covid-19 Image Classification"],"prefix":"10.1007","author":[{"given":"Manel","family":"Sekma","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amel","family":"Mhamdi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wady","family":"Naanaa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,9,21]]},"reference":[{"issue":"3","key":"27_CR1","doi-asserted-by":"publisher","first-page":"708","DOI":"10.1587\/transfun.E92.A.708","volume":"92","author":"A Cichocki","year":"2009","unstructured":"Cichocki, A., Phan, A.H.: Fast local algorithms for large scale nonnegative matrix and tensor factorizations. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 92(3), 708\u2013721 (2009)","journal-title":"IEICE Trans. Fundam. Electron. Commun. Comput. Sci."},{"key":"27_CR2","doi-asserted-by":"crossref","unstructured":"Cohen, J.P., Morrison, P., Dao, L., Roth, K., Duong, T.Q., Ghassemi, M.: Covid-19 image data collection: Prospective predictions are the future. arXiv preprint arXiv:2006.11988 (2020)","DOI":"10.59275\/j.melba.2020-48g7"},{"key":"27_CR3","doi-asserted-by":"crossref","unstructured":"Ding, C., He, X., Simon, H.D.: On the equivalence of nonnegative matrix factorization and spectral clustering. In: SIAM International Conference on Data Mining, pp. 606\u2013610 (2005)","DOI":"10.1137\/1.9781611972757.70"},{"key":"27_CR4","unstructured":"Gillis, N.: The why and how of nonnegative matrix factorization. CoRR abs\/1401.5226http:\/\/arxiv.org\/abs\/1401.5226 (2014)"},{"key":"27_CR5","doi-asserted-by":"crossref","unstructured":"Hartigan, J., Wong, M.: Algorithm AS 136: a K-means clustering algorithm. Appl. Stat. 28(1), 100\u2013108 (1979)","DOI":"10.2307\/2346830"},{"key":"27_CR6","doi-asserted-by":"crossref","unstructured":"Kim, H., Park, H.: Sparse non-negative matrix factorizations via alternating non-negativity-constrained least squares for microarray data analysis. Bioinformatics 23(12), 1495\u20131502 (2007)","DOI":"10.1093\/bioinformatics\/btm134"},{"issue":"2","key":"27_CR7","doi-asserted-by":"publisher","first-page":"713","DOI":"10.1137\/07069239X","volume":"30","author":"H Kim","year":"2008","unstructured":"Kim, H., Park, H.: Nonnegative matrix factorization based on alternating nonnegativity constrained least squares and active set method. SIAM J. Matrix Anal. Appl. 30(2), 713\u2013730 (2008)","journal-title":"SIAM J. Matrix Anal. Appl."},{"key":"27_CR8","doi-asserted-by":"crossref","unstructured":"Laurberg, H., Christensen, M.G., Plumbley, M.D., Hansen, L.K., Jensen, S.H.: Theorems on positive data: On the uniqueness of NMF. Comput. Intell. Neurosci. 2008, 764206 (2008)","DOI":"10.1155\/2008\/764206"},{"key":"27_CR9","doi-asserted-by":"publisher","first-page":"254","DOI":"10.1016\/j.sigpro.2016.07.016","volume":"130","author":"W Naanaa","year":"2017","unstructured":"Naanaa, W., Nuzillard, J.: Extreme direction analysis for blind separation of nonnegative signals. Signal Process. 130, 254\u2013267 (2017)","journal-title":"Signal Process."},{"issue":"2","key":"27_CR10","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1002\/env.3170050203","volume":"5","author":"P Pentti","year":"1994","unstructured":"Pentti, P.: Tapper unto: positive matrix factorization: a nonnegative factor model with optimal utilization of error estimates of data values. Environmetrics 5(2), 111\u2013126 (1994)","journal-title":"Environmetrics"},{"key":"27_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.mehy.2020.109761","volume":"140","author":"F Ucara","year":"2020","unstructured":"Ucara, F., Korkmaz, D.: COVIDiagnosis-Net: deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images. Med. Hypotheses 140, 109761 (2020)","journal-title":"Med. Hypotheses"},{"key":"27_CR12","doi-asserted-by":"crossref","unstructured":"Waheed, A., Goyal, M., Gupta, D., Khanna, A., Al-Turjman, F., Pinheiro, P.: CovidGAN: data augmentation using auxiliary classifier GAN for improved Covid-19 detection. IEEE Access 99, 1\u20131 (2020)","DOI":"10.1109\/ACCESS.2020.2994762"},{"key":"27_CR13","doi-asserted-by":"publisher","first-page":"4352","DOI":"10.1109\/JSTARS.2020.3010332","volume":"13","author":"Z Wang","year":"2020","unstructured":"Wang, Z., He, M., Wang, L., Xu, K., Xiao, J., Nian, Y.: Semi-NMF-based reconstruction for hyperspectral compressed sensing. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 13, 4352\u20134368 (2020). https:\/\/doi.org\/10.1109\/JSTARS.2020.3010332","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."}],"container-title":["Communications in Computer and Information Science","Advances in Computational Collective Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-16210-7_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T08:14:35Z","timestamp":1701072875000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16210-7_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031162091","9783031162107"],"references-count":13,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16210-7_27","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"21 September 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}