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However, these methods are designed for supervised learning problems when sufficient labeled data are available. Particularly for fast production rates, quality characteristics data tend to be scarcer than available process data generated through multiple sensors and automated data collection schemes. One way to overcome the problem of scarce outputs is to employ semi\u2010supervised learning methods, which use both labeled and unlabeled data. It has been shown that it is advantageous to use a semi\u2010supervised approach in case of labeled data and unlabeled data coming from the same distribution. In real applications, there is a chance that unlabeled data contain outliers or even a drift in the process, which will affect the performance of the semi\u2010supervised methods. The research question addressed in this work is how to detect outliers in the unlabeled data set using the scarce labeled data set. An iterative strategy is proposed using a combined Hotelling's T2<\/jats:sup> and Q statistics and applied using a semi\u2010supervised principal component regression (SS\u2010PCR) approach on both simulated and real data sets.<\/jats:p>","DOI":"10.1002\/qre.2522","type":"journal-article","created":{"date-parts":[[2019,7,11]],"date-time":"2019-07-11T11:06:13Z","timestamp":1562843173000},"page":"1408-1423","update-policy":"http:\/\/dx.doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Outliers detection using an iterative strategy for semi\u2010supervised learning"],"prefix":"10.1002","volume":"35","author":[{"ORCID":"http:\/\/orcid.org\/0000-0001-8596-7541","authenticated-orcid":false,"given":"Flavia D.","family":"Frumosu","sequence":"first","affiliation":[{"name":"Department of Applied Mathematics and Computer Science Technical University of Denmark Kgs. Lyngby Denmark"}]},{"given":"Murat","family":"Kulahci","sequence":"additional","affiliation":[{"name":"Department of Applied Mathematics and Computer Science Technical University of Denmark Kgs. Lyngby Denmark"},{"name":"Department of Business Administration, Technology and Social Sciences Lule\u00e5 University of Technology Lule\u00e5 Sweden"}]}],"member":"311","published-online":{"date-parts":[[2019,7,11]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12599-014-0334-4"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1021\/acs.iecr.7b04623"},{"issue":"8","key":"e_1_2_7_4_1","first-page":"771","article-title":"Multirate partial least squares for process monitoring","volume":"28","author":"Cong Y","year":"2015","journal-title":"9th IFAC Symp Adv Control of Chem Process 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