{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,2]],"date-time":"2025-11-02T19:22:06Z","timestamp":1762111326657},"reference-count":20,"publisher":"Springer Science and Business Media LLC","issue":"S8","content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2016,8]]},"DOI":"10.1186\/s12859-016-1120-8","type":"journal-article","created":{"date-parts":[[2016,8,31]],"date-time":"2016-08-31T03:23:57Z","timestamp":1472613837000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Bregman-proximal point algorithm for robust non-negative matrix factorization with possible missing values and outliers - application to gene expression analysis"],"prefix":"10.1186","volume":"17","author":[{"given":"St\u00e9phane","family":"Chr\u00e9tien","sequence":"first","affiliation":[]},{"given":"Christophe","family":"Guyeux","sequence":"additional","affiliation":[]},{"given":"Bastien","family":"Conesa","sequence":"additional","affiliation":[]},{"given":"R\u00e9gis","family":"Delage-Mouroux","sequence":"additional","affiliation":[]},{"given":"Mich\u00e8le","family":"Jouvenot","sequence":"additional","affiliation":[]},{"given":"Philippe","family":"Huetz","sequence":"additional","affiliation":[]},{"given":"Fran\u00e7oise","family":"Desc\u00f4tes","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2016,8,31]]},"reference":[{"key":"1120_CR1","doi-asserted-by":"crossref","unstructured":"Xu W, Liu X, Gong Y. Document clustering based on non-negative matrix factorization. In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Informaion Retrieval. ACM: 2003. p. 267\u201373.","DOI":"10.1145\/860435.860485"},{"issue":"3","key":"1120_CR2","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1007\/s10588-005-5380-5","volume":"11","author":"MW Berry","year":"2005","unstructured":"Berry MW, Browne M. Email surveillance using non-negative matrix factorization. Comput Math Organ Theory. 2005; 11(3):249\u201364.","journal-title":"Comput Math Organ Theory"},{"issue":"1","key":"1120_CR3","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1109\/TGRS.2008.2002882","volume":"47","author":"S Jia","year":"2009","unstructured":"Jia S, Qian Y. Constrained nonnegative matrix factorization for hyperspectral unmixing. Geosci Remote Sens IEEE Trans. 2009; 47(1):161\u201373.","journal-title":"Geosci Remote Sens IEEE Trans"},{"key":"1120_CR4","doi-asserted-by":"crossref","unstructured":"Guillamet D, Vitria J. Non-negative matrix factorization for face recognition. In: Topics in Artificial Intelligence. Springer: 2002. p. 336\u201344.","DOI":"10.1007\/3-540-36079-4_29"},{"issue":"10","key":"1120_CR5","doi-asserted-by":"crossref","first-page":"5120","DOI":"10.1109\/TSP.2008.928937","volume":"56","author":"TH Chan","year":"2008","unstructured":"Chan TH, Ma WK, Chi CY, Wang Y. A convex analysis framework for blind separation of non-negative sources. Signal Process IEEE Trans. 2008; 56(10):5120\u201334.","journal-title":"Signal Process IEEE Trans"},{"issue":"12","key":"1120_CR6","doi-asserted-by":"crossref","first-page":"1495","DOI":"10.1093\/bioinformatics\/btm134","volume":"23","author":"H Kim","year":"2007","unstructured":"Kim H, Park H. Sparse non-negative matrix factorizations via alternating non-negativity-constrained least squares for microarray data analysis. Bioinformatics. 2007; 23(12):1495\u2013502.","journal-title":"Bioinformatics"},{"issue":"3","key":"1120_CR7","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1002\/nbm.2850","volume":"26","author":"Y Li","year":"2013","unstructured":"Li Y, Sima DM, Cauter SV, Sava C, Anca R, Himmelreich U, Pi Y, Van Huffel S. Hierarchical non-negative matrix factorization (hnmf): a tissue pattern differentiation method for glioblastoma multiforme diagnosis using mrsi. NMR Biomed. 2013; 26(3):307\u201319.","journal-title":"NMR Biomed"},{"issue":"6755","key":"1120_CR8","doi-asserted-by":"crossref","first-page":"788","DOI":"10.1038\/44565","volume":"401","author":"DD Lee","year":"1999","unstructured":"Lee DD, Seung HS. Learning the parts of objects by non-negative matrix factorization. Nature. 1999; 401(6755):788\u201391.","journal-title":"Nature"},{"issue":"7","key":"1120_CR9","doi-asserted-by":"crossref","first-page":"3239","DOI":"10.1109\/TIP.2012.2190081","volume":"21","author":"E Esser","year":"2012","unstructured":"Esser E, M\u00f6ller M, Osher S, Sapiro G, Xin J. A convex model for nonnegative matrix factorization and dimensionality reduction on physical space. Image Process IEEE Trans. 2012; 21(7):3239\u201352.","journal-title":"Image Process IEEE Trans"},{"key":"1120_CR10","unstructured":"Recht B, Re C, Tropp J, Bittorf V. Factoring nonnegative matrices with linear programs. In: Advances in Neural Information Processing Systems: 2012. p. 1214\u201322."},{"issue":"4","key":"1120_CR11","doi-asserted-by":"crossref","first-page":"698","DOI":"10.1109\/TPAMI.2013.226","volume":"36","author":"N Gillis","year":"2014","unstructured":"Gillis N, Vavasis SA. Fast and robust recursive algorithmsfor separable nonnegative matrix factorization. Pattern Analysis and Machine Intelligence, IEEE Transactions on. 2014; 36(4):698\u2013714.","journal-title":"Pattern Analysis and Machine Intelligence, IEEE Transactions on"},{"key":"1120_CR12","unstructured":"Sra S, Dhillon IS. Generalized nonnegative matrix approximations with bregman divergences. In: Advances in Neural Information Processing Systems: 2005. p. 283\u201390."},{"key":"1120_CR13","doi-asserted-by":"crossref","unstructured":"Li L, Lebanon G, Park H. Fast bregman divergence nmf using taylor expansion and coordinate descent. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM: 2012. p. 307\u201315.","DOI":"10.1145\/2339530.2339582"},{"issue":"3-4","key":"1120_CR14","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1561\/2200000050","volume":"8","author":"S Bubeck","year":"2015","unstructured":"Bubeck S. Convex optimization: Algorithms and complexity. Foundations and Trends\u00ae; in Machine Learning. 2015; 8(3-4):231\u2013357.","journal-title":"Foundations and Trends\u00ae; in Machine Learning"},{"issue":"3","key":"1120_CR15","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1145\/1970392.1970395","volume":"58","author":"EJ Cand\u00e8s","year":"2011","unstructured":"Cand\u00e8s EJ, Li X, Ma Y, Wright J. Robust principal component analysis?J ACM (JACM). 2011; 58(3):11.","journal-title":"J ACM (JACM)"},{"key":"1120_CR16","unstructured":"Zhou T, Tao D. Godec: Randomized low-rank & sparse matrix decomposition in noisy case. In: International Conference on Machine Learning. Omnipress: 2011."},{"issue":"3","key":"1120_CR17","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1561\/2400000003","volume":"1","author":"N Parikh","year":"2014","unstructured":"Parikh N, Boyd SP. Proximal algorithms. Foundations Trends Optim. 2014; 1(3):127\u2013239.","journal-title":"Foundations Trends Optim"},{"key":"1120_CR18","first-page":"257","volume":"12","author":"N Gillis","year":"2014","unstructured":"Gillis N. The why and how of nonnegative matrix factorization. Regularization Optim Kernels Support Vector Mach. 2014; 12:257.","journal-title":"Regularization Optim Kernels Support Vector Mach"},{"key":"1120_CR19","unstructured":"Husson F, Josse J. missmda: Handling missing values with\/in multivariate data analysis (principal component methods). R package version. 2010; 1(2)."},{"issue":"2","key":"1120_CR20","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1111\/bju.12364","volume":"113","author":"F Descotes","year":"2014","unstructured":"Descotes F, Dessen P, Bringuier PP, Decaussin M, Martin PM, Adams M, Villers A, Lechevallier E, Rebillard X, Rodriguez-Lafrasse C, et al.Microarray gene expression profiling and analysis of bladder cancer supports the sub-classification of t1 tumours into t1a and t1b stages. BJU Intl. 2014; 113(2):333\u201342.","journal-title":"BJU Intl"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-016-1120-8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2017,6,24]],"date-time":"2017-06-24T17:43:15Z","timestamp":1498326195000},"score":1,"resource":{"primary":{"URL":"http:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-016-1120-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,8]]},"references-count":20,"journal-issue":{"issue":"S8","published-print":{"date-parts":[[2016,8]]}},"alternative-id":["1120"],"URL":"https:\/\/doi.org\/10.1186\/s12859-016-1120-8","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,8]]},"article-number":"284"}}