{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T03:04:22Z","timestamp":1782875062306,"version":"3.54.5"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2022,9,17]],"date-time":"2022-09-17T00:00:00Z","timestamp":1663372800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,9,17]],"date-time":"2022-09-17T00:00:00Z","timestamp":1663372800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Key Project of Hunan Provincial Department of Education","award":["No.21A0403"],"award-info":[{"award-number":["No.21A0403"]}]},{"DOI":"10.13039\/501100004735","name":"Natural Science Foundation of Hunan Province","doi-asserted-by":"publisher","award":["No.2022JJ30282"],"award-info":[{"award-number":["No.2022JJ30282"]}],"id":[{"id":"10.13039\/501100004735","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Project of Hunan Provincial Department of Education","award":["No.21A0405"],"award-info":[{"award-number":["No.21A0405"]}]},{"name":"University-industrycollaborativeproject","award":["No.202102211006"],"award-info":[{"award-number":["No.202102211006"]}]},{"name":"Key Laboratory of Hunan Province","award":["No.2019TP1014"],"award-info":[{"award-number":["No.2019TP1014"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2023,2]]},"DOI":"10.1007\/s13042-022-01649-w","type":"journal-article","created":{"date-parts":[[2022,9,17]],"date-time":"2022-09-17T06:02:36Z","timestamp":1663394556000},"page":"551-566","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A novel adaptive methodology for removing spurious components in a modified incremental Gaussian mixture model"],"prefix":"10.1007","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2712-5199","authenticated-orcid":false,"given":"Shuping","family":"Sun","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaonan","family":"Tong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Biqiang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bowen","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Long","family":"Yan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peiguang","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,9,17]]},"reference":[{"key":"1649_CR1","doi-asserted-by":"publisher","first-page":"947","DOI":"10.1109\/TCBB.2016.2561927","volume":"14","author":"J Zhang","year":"2017","unstructured":"Zhang J, Yin Z, Wang R (2017) Pattern classification of instantaneous cognitive task-load through GMM clustering, Laplacian Eigenmap, and ensemble SVMs. IEEE\/ACM Trans Comput Biol Bioinf 14:947\u2013965","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf"},{"key":"1649_CR2","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.neucom.2016.08.147","volume":"269","author":"Z Li","year":"2017","unstructured":"Li Z, Xia Y, Ji Z, Zhang Y (2017) Brain voxel classification in magnetic resonance images using niche differential evolution based Bayesian inference of variational mixture of Gaussians. Neurocomputing 269:47\u201357","journal-title":"Neurocomputing"},{"key":"1649_CR3","doi-asserted-by":"publisher","first-page":"226","DOI":"10.1016\/j.bbr.2016.09.022","volume":"317","author":"A Ortiz-Rosario","year":"2017","unstructured":"Ortiz-Rosario A, Adeli H, Buford JA (2017) MUSIC-expected maximization gaussian mixture methodology for clustering and detection of task-related neuronal firing rates. Behav Brain Res 317:226\u2013236","journal-title":"Behav Brain Res"},{"key":"1649_CR4","doi-asserted-by":"publisher","first-page":"942","DOI":"10.1109\/LGRS.2018.2817361","volume":"15","author":"A Davari","year":"2018","unstructured":"Davari A, Aptoula E, Yanikoglu B, Maier A, Riess C (2018) GMM-based synthetic samples for classification of hyperspectral images with limited training data. IEEE Geosci Remote Sens Lett 15:942\u2013946","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"1649_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.nima.2018.05.039","volume":"900","author":"LM Simms","year":"2018","unstructured":"Simms LM, Blair B, Ruz J, Wurtz R, Kaplan AD, Glenn A (2018) A pulse discrimination with a Gaussian mixture model on an FPGA. Nucl Inst Methods Phys Res A 900:1\u20137","journal-title":"Nucl Inst Methods Phys Res A"},{"key":"1649_CR6","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1016\/j.measurement.2018.04.019","volume":"124","author":"W Xue","year":"2018","unstructured":"Xue W, Jiang T (2018) An adaptive algorithm for target recognition using Gaussian mixture models. Meas J Int Meas Confed 124:233\u2013240","journal-title":"Meas J Int Meas Confed"},{"key":"1649_CR7","doi-asserted-by":"crossref","unstructured":"Heinen MR, Engel PM, Pinto RC (2012) Using a gaussian mixture neural network for incremental learning and robotics. In: The 2012 international joint conference on neural networks (IJCNN), pp 1\u20138","DOI":"10.1109\/IJCNN.2012.6252399"},{"key":"1649_CR8","unstructured":"Heinen MR, Engel PM, Pinto RC (2011) IGMN: an incremental gaussian mixture network that learns instantaneously from data flows. In: Proc VIII Encontro Nacional de Intelig\u00eancia Artificial (ENIA2011)"},{"key":"1649_CR9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.2517-6161.1977.tb01600.x","volume":"39","author":"AP Dempster","year":"1977","unstructured":"Dempster AP (1977) Maximum likelihood estimation from incomplete data via the EM algorithm. J R Stat Soc Ser B (Stat Methodol) 39:1\u201338","journal-title":"J R Stat Soc Ser B (Stat Methodol)"},{"key":"1649_CR10","doi-asserted-by":"crossref","unstructured":"Engel PM, Heinen MR Incremental learning of multivariate gaussian mixture models. In: Brazilian symposium on artificial intelligence. Springer, pp 82\u201391","DOI":"10.1007\/978-3-642-16138-4_9"},{"key":"1649_CR11","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1111\/j.1551-6708.1987.tb00862.x","volume":"11","author":"S Grossberg","year":"1987","unstructured":"Grossberg S (1987) Competitive learning: from interactive activation to adaptive resonance. Cogn Sci 11:23\u201363","journal-title":"Cogn Sci"},{"issue":"10","key":"1649_CR12","doi-asserted-by":"publisher","first-page":"e0141942","DOI":"10.1371\/journal.pone.0141942","volume":"10","author":"RC Pinto","year":"2015","unstructured":"Pinto RC, Engel PM (2015) A fast incremental gaussian mixture model. PLOS ONE 10(10):e0141942","journal-title":"PLOS ONE"},{"key":"1649_CR13","doi-asserted-by":"crossref","unstructured":"Pragr M, Cizek P (2018) Cost of transport estimation for legged robot based on terrain features inference from aerial scan. In: IEEE international conference on intelligent robots and systems, pp 1745\u20131750","DOI":"10.1109\/IROS.2018.8593374"},{"key":"1649_CR14","doi-asserted-by":"crossref","unstructured":"Pr\u00e1gr M, \u010c\u00ed\u017eek P (2019) Incremental learning of traversability cost for aerial reconnaissance support to ground units, lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics) 11472 LNCS, pp 412\u2013421","DOI":"10.1007\/978-3-030-14984-0_30"},{"key":"1649_CR15","unstructured":"Zhao R, Li Y, Sun Y (2018) Statistical convergence of the EM algorithm on Gaussian mixture models. http:\/\/arxiv.org\/abs\/1810.04090"},{"key":"1649_CR16","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1214\/aoms\/1177729586","volume":"22","author":"H Robbins","year":"1951","unstructured":"Robbins H, Monro S (1951) A stochastic approximation method. Ann Math Stat 22:400\u2013407","journal-title":"Ann Math Stat"},{"key":"1649_CR17","first-page":"1","volume":"10","author":"RC Pinto","year":"2015","unstructured":"Pinto RC, Engel PM (2015) A fast incremental gaussian mixture model. PLoS ONE 10:1\u201312","journal-title":"PLoS ONE"},{"key":"1649_CR18","doi-asserted-by":"crossref","unstructured":"Chamby-Diaz CJ, Recamonde-Mendoza M, Bazzan LCA, Grunitzki R (2018) Adaptive incremental gaussian mixture network for non-stationary data stream classification. In: Proceedings of the international joint conference on neural networks 2018-July, pp 1\u20138","DOI":"10.1109\/IJCNN.2018.8489049"},{"key":"1649_CR19","doi-asserted-by":"crossref","unstructured":"Koert D, Trick S, Ewerton M, Lutter (2019) Online learning of an open-ended skill library for collaborative tasks. In: IEEE-RAS international conference on humanoid robots 2018-November, pp 599\u2013606","DOI":"10.1109\/HUMANOIDS.2018.8625031"},{"key":"1649_CR20","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1007\/s11053-018-9399-y","volume":"28","author":"DA Drumond","year":"2019","unstructured":"Drumond DA, Rolo RM, Costa JFCL (2019) Using Mahalanobis distance to detect and remove outliers in experimental covariograms. Nat Resour Res 28:145\u2013152","journal-title":"Nat Resour Res"},{"key":"1649_CR21","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1109\/TPWRS.2009.2030271","volume":"25","author":"R Singh","year":"2009","unstructured":"Singh R, Pal BC, Jabr RA (2009) Statistical representation of distribution system loads using gaussian mixture model. IEEE Transa Power Syst 25:29\u201337","journal-title":"IEEE Transa Power Syst"},{"key":"1649_CR22","first-page":"775","volume":"2","author":"DJ Salmond","year":"1989","unstructured":"Salmond DJ, Atherton DP, Bather JA (1989) Mixture reduction algorithms for uncertain tracking. IFAC Proc Ser 2:775\u2013780","journal-title":"IFAC Proc Ser"},{"key":"1649_CR23","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/j.jtbi.2016.04.022","volume":"402","author":"F Pro\u00efa","year":"2016","unstructured":"Pro\u00efa F, Pernet A, Thouroude T, Michel G, Clotault J (2016) On the characterization of flowering curves using Gaussian mixture models. J Theor Biol 402:75\u201388","journal-title":"J Theor Biol"},{"key":"1649_CR24","doi-asserted-by":"crossref","unstructured":"Mungai PK (2017) Using keystroke dynamics in a multi-level architecture to protect online examinations from impersonation. In: 2017 IEEE 2nd international conference on big data analysis, pp 622\u2013627","DOI":"10.1109\/ICBDA.2017.8078710"},{"issue":"2","key":"1649_CR25","first-page":"327","volume":"10","author":"A Aryafar","year":"2019","unstructured":"Aryafar A, Mikaeil R, Ardejani FD, Haghshenas SS, Jafarpour A (2019) Application of non-linear regression and soft computing techniques for modeling process of pollutant adsorption from industrial wastewaters. J Min Environ 10(2):327\u2013337","journal-title":"J Min Environ"},{"key":"1649_CR26","doi-asserted-by":"publisher","first-page":"2665","DOI":"10.1109\/JSEN.2018.2882582","volume":"19","author":"S Sun","year":"2019","unstructured":"Sun S, Wang H, Chang Z, Mao B, Liu Y (2019) On the Mahalanobis distance classification criterion for a ventricular septal defect diagnosis system. IEEE Sens J 19:2665\u20132674","journal-title":"IEEE Sens J"},{"key":"1649_CR27","doi-asserted-by":"publisher","first-page":"4453","DOI":"10.12733\/jics20102195","volume":"10","author":"C Xie","year":"2013","unstructured":"Xie C, Chang J, Liu Y (2013) Estimating the number of components in Gaussian mixture models adaptively. J Inf Comput Sci 10:4453\u20134460","journal-title":"J Inf Comput Sci"},{"key":"1649_CR28","first-page":"49","volume":"62","author":"C Keribin","year":"2000","unstructured":"Keribin C (2000) Consistent estimate of the order of mixture models. Sankhy A Ser A 62:49\u201366","journal-title":"Sankhy A Ser A"},{"key":"1649_CR29","doi-asserted-by":"publisher","first-page":"716","DOI":"10.1109\/TAC.1974.1100705","volume":"19","author":"H Akaike","year":"1974","unstructured":"Akaike H (1974) A new look at the statistical model identification. IEEE Trans Autom Control 19:716\u2013723","journal-title":"IEEE Trans Autom Control"},{"key":"1649_CR30","doi-asserted-by":"publisher","first-page":"1799","DOI":"10.1016\/j.patrec.2004.07.007","volume":"25","author":"H Wang","year":"2004","unstructured":"Wang H, Luo B, Zhang Q, Wei S (2004) Estimation for the number of components in a mixture model using stepwise split-and-merge EM algorithm. Pattern Recognit Lett 25:1799\u20131809","journal-title":"Pattern Recognit Lett"},{"key":"1649_CR31","doi-asserted-by":"publisher","first-page":"260","DOI":"10.1007\/11840930_27","volume-title":"Artificial neural networks\u2014ICANN 2006","author":"L Shi","year":"2006","unstructured":"Shi L, Xu L (2006) Local factor analysis with automatic model selection: a comparative study and digits recognition application. In: Kollias S, Stafylopatis A, Duch W, Oja E (eds) Artificial neural networks\u2014ICANN 2006. Springer, Berlin, pp 260\u2013269"},{"key":"1649_CR32","doi-asserted-by":"publisher","unstructured":"Pinto R (2015) Experiment data for \u201cA Fast Incremental Gaussian Mixture Model\u201d. https:\/\/doi.org\/10.6084\/M9.FIGSHARE.1552030.V2","DOI":"10.6084\/M9.FIGSHARE.1552030.V2"},{"key":"1649_CR33","doi-asserted-by":"publisher","first-page":"6035","DOI":"10.1007\/s00500-018-3076-2","volume":"22","author":"S Deb","year":"2018","unstructured":"Deb S, Tian Z, Fong S, Wong R, Millham R, Wong KK (2018) Elephant search algorithm applied to data clustering. Soft Comput 22:6035\u20136046","journal-title":"Soft Comput"},{"key":"1649_CR34","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V et al (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825\u20132830","journal-title":"J Mach Learn Res"},{"key":"1649_CR35","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y Lecun","year":"1998","unstructured":"Lecun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86:2278\u20132324","journal-title":"Proc IEEE"},{"key":"1649_CR36","unstructured":"Krizhevsky HGLA (2009) Learning multiple layers of features from tiny images. Technical Report, Computer Science Department, University of Toronto"},{"key":"1649_CR37","doi-asserted-by":"publisher","first-page":"16505","DOI":"10.1007\/s00521-019-04163-3","volume":"32","author":"H Kusetogullari","year":"2020","unstructured":"Kusetogullari H, Yavariabdi A, Cheddad A, Grahn H, Hall J (2020) ARDIS: a Swedish historical handwritten digit dataset. Neural Comput Appl 32:16505\u201316518","journal-title":"Neural Comput Appl"},{"key":"1649_CR38","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1016\/j.knosys.2016.10.002","volume":"114","author":"Y Wang","year":"2016","unstructured":"Wang Y, Chaib-draa B (2016) KNN-based Kalman filter: an efficient and non-stationary method for Gaussian process regression. Knowl-Based Syst 114:148\u2013155","journal-title":"Knowl-Based Syst"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-022-01649-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-022-01649-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-022-01649-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,4]],"date-time":"2024-10-04T01:34:14Z","timestamp":1728005654000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-022-01649-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,17]]},"references-count":38,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,2]]}},"alternative-id":["1649"],"URL":"https:\/\/doi.org\/10.1007\/s13042-022-01649-w","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,17]]},"assertion":[{"value":"1 August 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 August 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 September 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}