{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T04:27:31Z","timestamp":1770697651036,"version":"3.49.0"},"publisher-location":"Singapore","reference-count":30,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819569564","type":"print"},{"value":"9789819569571","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-981-95-6957-1_25","type":"book-chapter","created":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T10:45:26Z","timestamp":1770633926000},"page":"346-360","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Low-Dimension Representation Estimation in\u00a0Principal Component Analysis Under Missing Data"],"prefix":"10.1007","author":[{"given":"Thanh Tu","family":"Do","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Van","family":"Hua","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Uyen","family":"Dang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thu","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Steven","family":"Hicks","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"P\u00e5l","family":"Halvorsen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael A.","family":"Riegler","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Binh T.","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,2,10]]},"reference":[{"key":"25_CR1","unstructured":"et\u00a0al, C.: SPECTF Heart. UCI Machine Learning Repository (2001). https:\/\/doi.org\/10.24432\/C5N015"},{"key":"25_CR2","unstructured":"et\u00a0al, C.: Anuran Calls (MFCCs). UCI Machine Learning Repository (2017). https:\/\/doi.org\/10.24432\/C5CC9H"},{"key":"25_CR3","unstructured":"et\u00a0al, S.: Ionosphere. UCI Machine Learning Repository (1989). https:\/\/doi.org\/10.24432\/C5W01B"},{"key":"25_CR4","doi-asserted-by":"crossref","unstructured":"Almeida, R.J., Adriaans, G., Shapovalova, Y.: Graphical causal models and imputing missing data: a preliminary study. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, pp. 485\u2013496. Springer (2020)","DOI":"10.1007\/978-3-030-50146-4_36"},{"key":"25_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.07.009","volume":"182","author":"SJ Choudhury","year":"2019","unstructured":"Choudhury, S.J., Pal, N.R.: Imputation of missing data with neural networks for classification. Knowl.-Based Syst. 182, 104838 (2019)","journal-title":"Knowl.-Based Syst."},{"key":"25_CR6","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/j.chemolab.2015.05.006","volume":"146","author":"A Folch-Fortuny","year":"2015","unstructured":"Folch-Fortuny, A., Arteaga, F., Ferrer, A.: PCA model building with missing data: new proposals and a comparative study. Chemom. Intell. Lab. Syst. 146, 77\u201388 (2015)","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"25_CR7","doi-asserted-by":"publisher","unstructured":"Friedman, J., Hastie, T., Tibshirani, R.: Sparse inverse covariance estimation with the graphical lasso. Biostatistics 9(3), 432\u2013441 (12 2007). https:\/\/doi.org\/10.1093\/biostatistics\/kxm045","DOI":"10.1093\/biostatistics\/kxm045"},{"key":"25_CR8","first-page":"149","volume":"138","author":"J Gower","year":"1971","unstructured":"Gower, J.: Statistical methods of comparing different multivariate analyses of the same data. Math. Archaeol. Historical Sci. 138, 149 (1971)","journal-title":"Math. Archaeol. Historical Sci."},{"issue":"6","key":"25_CR9","doi-asserted-by":"publisher","first-page":"651","DOI":"10.1007\/s13258-022-01247-8","volume":"44","author":"H Jin","year":"2022","unstructured":"Jin, H., Jung, S., Won, S.: missforest with feature selection using binary particle swarm optimization improves the imputation accuracy of continuous data. Genes & Genomics 44(6), 651\u2013658 (2022)","journal-title":"Genes & Genomics"},{"key":"25_CR10","volume-title":"Applied Multivariate Statistical Analysis","author":"RA Johnson","year":"2002","unstructured":"Johnson, R.A., Wichern, D.W., et al.: Applied Multivariate Statistical Analysis, vol. 5. Prentice hall Upper Saddle River, NJ (2002)"},{"issue":"3","key":"25_CR11","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1007\/s11634-011-0086-7","volume":"5","author":"J Josse","year":"2011","unstructured":"Josse, J., Husson, F., et al.: Multiple imputation in principal component analysis. Adv. Data Anal. Classif. 5(3), 231\u2013246 (2011)","journal-title":"Adv. Data Anal. Classif."},{"key":"25_CR12","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1007\/BF02295279","volume":"62","author":"HA Kiers","year":"1997","unstructured":"Kiers, H.A.: Weighted least squares fitting using ordinary least squares algorithms. Psychometrika 62, 251\u2013266 (1997)","journal-title":"Psychometrika"},{"key":"25_CR13","doi-asserted-by":"publisher","unstructured":"Ledoit, O., Wolf, M.: A well-conditioned estimator for large-dimensional covariance matrices. J. Multivariate Anal. 88(2), 365\u2013411 (2004). https:\/\/doi.org\/10.1016\/S0047-259X(03)00096-4, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0047259X03000964","DOI":"10.1016\/S0047-259X(03)00096-4"},{"key":"25_CR14","doi-asserted-by":"publisher","first-page":"1487","DOI":"10.1007\/s10462-019-09709-4","volume":"53","author":"WC Lin","year":"2020","unstructured":"Lin, W.C., Tsai, C.F.: Missing value imputation: a review and analysis of the literature (2006\u20132017). Artif. Intell. Rev. 53, 1487\u20131509 (2020)","journal-title":"Artif. Intell. Rev."},{"key":"25_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.108079","volume":"239","author":"WC Lin","year":"2022","unstructured":"Lin, W.C., Tsai, C.F., Zhong, J.R.: Deep learning for missing value imputation of continuous data and the effect of data discretization. Knowl.-Based Syst. 239, 108079 (2022)","journal-title":"Knowl.-Based Syst."},{"key":"25_CR16","unstructured":"Mazumder, R., Hastie, T., Tibshirani, R.: Spectral regularization algorithms for learning large incomplete matrices. J. Mach. Learn. Res. 11(Aug), 2287\u20132322 (2010)"},{"key":"25_CR17","unstructured":"Nakai, K.: Ecoli. UCI Machine Learning Repository (1996). https:\/\/doi.org\/10.24432\/C5388M"},{"key":"25_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ins.2021.02.077","volume":"567","author":"T Nguyen","year":"2021","unstructured":"Nguyen, T., Nguyen, D.H., Nguyen, H., Nguyen, B.T., Wade, B.A.: EPEM: efficient parameter estimation for multiple class monotone missing data. Inf. Sci. 567, 1\u201322 (2021)","journal-title":"Inf. Sci."},{"key":"25_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.108082","volume":"240","author":"T Nguyen","year":"2022","unstructured":"Nguyen, T., Nguyen-Duy, K.M., Nguyen, D.H.M., Nguyen, B.T., Wade, B.A.: DPER: direct parameter estimation for randomly missing data. Knowl.-Based Syst. 240, 108082 (2022)","journal-title":"Knowl.-Based Syst."},{"key":"25_CR20","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"key":"25_CR21","doi-asserted-by":"crossref","unstructured":"Pujianto, U., Wibawa, A.P., Akbar, M.I., et\u00a0al.: K-nearest neighbor (k-nn) based missing data imputation. In: 2019 5th International Conference on Science in Information Technology (ICSITech), pp. 83\u201388. IEEE (2019)","DOI":"10.1109\/ICSITech46713.2019.8987530"},{"key":"25_CR22","doi-asserted-by":"publisher","first-page":"1741","DOI":"10.1007\/s00180-019-00900-3","volume":"34","author":"B Ramosaj","year":"2019","unstructured":"Ramosaj, B., Pauly, M.: Predicting missing values: a comparative study on non-parametric approaches for imputation. Comput. Stat. 34, 1741\u20131764 (2019)","journal-title":"Comput. Stat."},{"key":"25_CR23","unstructured":"Rubinsteyn, A., Feldman, S.: fancyimpute: An imputation library for python. https:\/\/github.com\/iskandr\/fancyimpute (2016)"},{"key":"25_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.108968","volume":"249","author":"MD Samad","year":"2022","unstructured":"Samad, M.D., Abrar, S., Diawara, N.: Missing value estimation using clustering and deep learning within multiple imputation framework. Knowl.-Based Syst. 249, 108968 (2022)","journal-title":"Knowl.-Based Syst."},{"issue":"1","key":"25_CR25","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1093\/bioinformatics\/btr597","volume":"28","author":"DJ Stekhoven","year":"2012","unstructured":"Stekhoven, D.J., B\u00fchlmann, P.: Missforest-non-parametric missing value imputation for mixed-type data. Bioinformatics 28(1), 112\u2013118 (2012)","journal-title":"Bioinformatics"},{"key":"25_CR26","first-page":"24804","volume":"34","author":"Y Tashiro","year":"2021","unstructured":"Tashiro, Y., Song, J., Song, Y., Ermon, S.: CSDI: conditional score-based diffusion models for probabilistic time series imputation. Adv. Neural. Inf. Process. Syst. 34, 24804\u201324816 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"25_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v045.i03","volume":"45","author":"S Van Buuren","year":"2011","unstructured":"Van Buuren, S., Groothuis-Oudshoorn, K.: mice: multivariate imputation by chained equations in r. J. Stat. Softw. 45, 1\u201367 (2011)","journal-title":"J. Stat. Softw."},{"key":"25_CR28","unstructured":"Vu, M.A., Nguyen, T., Do, T.T., Phan, N., Halvorsen, P., Riegler, M.A., Nguyen, B.T.: Conditional expectation for missing data imputation. arXiv preprint arXiv:2302.00911 (2023)"},{"issue":"1","key":"25_CR29","first-page":"29","volume":"43","author":"H Wold","year":"1969","unstructured":"Wold, H., Lyttkens, E.: Nonlinear iterative partial least squares (nipals) estimation procedures. Bull. Int. Stat. Inst. 43(1), 29\u201347 (1969)","journal-title":"Bull. Int. Stat. Inst."},{"key":"25_CR30","unstructured":"Yoon, J., Jordon, J., Schaar, M.: Gain: Missing data imputation using generative adversarial nets. In: International Conference on Machine Learning, pp. 5689\u20135698. PMLR (2018)"}],"container-title":["Lecture Notes in Computer Science","MultiMedia Modeling"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-6957-1_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T10:45:30Z","timestamp":1770633930000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-6957-1_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819569564","9789819569571"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-6957-1_25","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"10 February 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MMM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Multimedia Modeling","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Prague","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Czech Republic","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 January 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31 January 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"32","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mmm2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/mmm2026.cz\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}