{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T19:40:54Z","timestamp":1761766854832,"version":"3.37.3"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2021,1,18]],"date-time":"2021-01-18T00:00:00Z","timestamp":1610928000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,18]],"date-time":"2021-01-18T00:00:00Z","timestamp":1610928000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"name":"National key research and development program of China","award":["2020YFA0908300"],"award-info":[{"award-number":["2020YFA0908300"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["21878081"],"award-info":[{"award-number":["21878081"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multidim Syst Sign Process"],"published-print":{"date-parts":[[2021,4]]},"DOI":"10.1007\/s11045-020-00758-5","type":"journal-article","created":{"date-parts":[[2021,1,18]],"date-time":"2021-01-18T21:03:00Z","timestamp":1611003780000},"page":"767-789","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Visual high dimensional industrial process monitoring based on deep discriminant features and t-SNE"],"prefix":"10.1007","volume":"32","author":[{"given":"Weipeng","family":"Lu","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5622-8686","authenticated-orcid":false,"given":"Xuefeng","family":"Yan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,1,18]]},"reference":[{"key":"758_CR1","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1007\/978-94-011-3222-0_42","volume":"335","author":"AR Barron","year":"1991","unstructured":"Barron, A. R. (1991). Complexity regularization with application to artificial neural networks. Nonparametric Functional Estimation and Related Topics, 335, 561\u2013576.","journal-title":"Nonparametric Functional Estimation and Related Topics"},{"key":"758_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.gmod.2020.101060","author":"M Becker","year":"2020","unstructured":"Becker, M., Lippel, J., Stuhlsatz, A., & Zielke, T. (2020). Robust dimensionality reduction for data visualization with deep neural networks. Graphical Models. https:\/\/doi.org\/10.1016\/j.gmod.2020.101060.","journal-title":"Graphical Models"},{"key":"758_CR3","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1107\/S1600577517018148","volume":"25","author":"D Borek","year":"2018","unstructured":"Borek, D., Bromberg, R., Hattne, J., & Otwinowski, Z. (2018). Real-space analysis of radiation-induced specific changes with independent component analysis. Journal of Synchrotron Radiation, 25, 451\u2013467. https:\/\/doi.org\/10.1107\/S1600577517018148.","journal-title":"Journal of Synchrotron Radiation"},{"issue":"3","key":"758_CR4","doi-asserted-by":"publisher","first-page":"476","DOI":"10.1002\/ajpa.23130","volume":"162","author":"SE Calce","year":"2017","unstructured":"Calce, S. E., Kurki, H. K., Weston, D. A., & Gould, L. (2017). Principal component analysis in the evaluation of osteoarthritis. American Journal of Physical Anthropology, 162(3), 476\u2013490. https:\/\/doi.org\/10.1002\/ajpa.23130.","journal-title":"American Journal of Physical Anthropology"},{"issue":"12","key":"758_CR5","doi-asserted-by":"publisher","first-page":"2262","DOI":"10.1016\/j.cherd.2012.06.004","volume":"90","author":"XY Chen","year":"2012","unstructured":"Chen, X. Y., & Yan, X. F. (2012). Using improved self-organizing map for fault diagnosis in chemical industry process. Chemical Engineering Research and Design, 90(12), 2262\u20132277. https:\/\/doi.org\/10.1016\/j.cherd.2012.06.004.","journal-title":"Chemical Engineering Research and Design"},{"issue":"4","key":"758_CR6","doi-asserted-by":"publisher","first-page":"382","DOI":"10.1016\/S1004-9541(13)60469-3","volume":"21","author":"XY Chen","year":"2013","unstructured":"Chen, X. Y., & Yan, X. F. (2013). Fault diagnosis in chemical process based on self-organizing map integrated with fisher discriminant analysis. Chinese Journal of Chemical Engineering, 21(4), 382\u2013387. https:\/\/doi.org\/10.1016\/S1004-9541(13)60469-3.","journal-title":"Chinese Journal of Chemical Engineering"},{"issue":"8","key":"758_CR7","doi-asserted-by":"publisher","first-page":"1389","DOI":"10.1016\/j.compchemeng.2003.10.002","volume":"28","author":"LH Chiang","year":"2004","unstructured":"Chiang, L. H., Kotanchek, M. E., & Kordon, A. K. (2004). Fault diagnosis based on Fisher discriminant analysis and support vector machines. Computers & Chemical Engineering, 28(8), 1389\u20131401. https:\/\/doi.org\/10.1016\/j.compchemeng.2003.10.002.","journal-title":"Computers & Chemical Engineering"},{"key":"758_CR8","doi-asserted-by":"crossref","unstructured":"Chiang, L. H., Russell, E. L., & Braatz, R. D. (2001). Fault detection and diagnosis in industrial systems. In Springer, 2001 advanced textbooks in control and signal processing.","DOI":"10.1007\/978-1-4471-0347-9"},{"issue":"12","key":"758_CR9","doi-asserted-by":"publisher","first-page":"2022","DOI":"10.1016\/j.compchemeng.2010.07.002","volume":"34","author":"F Corona","year":"2010","unstructured":"Corona, F., Mulas, M., Baratti, R., & Romagnoli, J. A. (2010). On the topological modeling and analysis of industrial process data using the SOM. Computers & Chemical Engineering, 34(12), 2022\u20132032. https:\/\/doi.org\/10.1016\/j.compchemeng.2010.07.002.","journal-title":"Computers & Chemical Engineering"},{"key":"758_CR10","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-018-04368-5","author":"JR Ding","year":"2018","unstructured":"Ding, J. R., Condon, A., & Shah, S. P. (2018). Interpretable dimensionality reduction of single cell transcriptome data with deep generative models. Nature Communications. https:\/\/doi.org\/10.1038\/s41467-018-04368-5.","journal-title":"Nature Communications"},{"key":"758_CR11","unstructured":"Dorfer, M., Kelz, R., & Widmer, G. (2016). Deep linear discriminant analysis. International Conference on Learning Representations 2016."},{"issue":"2","key":"758_CR12","doi-asserted-by":"publisher","first-page":"622","DOI":"10.1364\/Boe.10.000622","volume":"10","author":"VA Dos Santos","year":"2019","unstructured":"Dos Santos, V. A., Schmetterer, L., Stegmann, H., Pfister, M., Messner, A., Schmidinger, G., et al. (2019). CorneaNet: fast segmentation of cornea OCT scans of healthy and keratoconic eyes using deep learning. Biomedical Optics Express, 10(2), 622\u2013641. https:\/\/doi.org\/10.1364\/Boe.10.000622.","journal-title":"Biomedical Optics Express"},{"issue":"3","key":"758_CR13","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1016\/0098-1354(93)80018-I","volume":"17","author":"JJ Downs","year":"1993","unstructured":"Downs, J. J., & Vogel, E. F. (1993). A plant-wide industrial process control problem. Computers & Chemical Engineering, 17(3), 245\u2013255.","journal-title":"Computers & Chemical Engineering"},{"key":"758_CR14","doi-asserted-by":"publisher","unstructured":"Flamini, F., Spagnolo, N., & Sciarrino, F. (2019). Visual assessment of multi-photon interference. Quantum Science and Technology, 4(2). https:\/\/doi.org\/10.1088\/2058-9565\/ab04fc.","DOI":"10.1088\/2058-9565\/ab04fc"},{"key":"758_CR15","doi-asserted-by":"publisher","unstructured":"Galiaskarov, M. R., Kurkina, V. V., & Rusinov, L. A. (2017). Online diagnostics of time-varying nonlinear chemical processes using moving window kernel principal component analysis and Fisher discriminant analysis. Journal of Chemometrics, 31(8). https:\/\/doi.org\/10.1002\/cem.2866.","DOI":"10.1002\/cem.2866"},{"issue":"2","key":"758_CR16","doi-asserted-by":"publisher","first-page":"2157","DOI":"10.1007\/s11042-018-6273-1","volume":"78","author":"M Gnouma","year":"2019","unstructured":"Gnouma, M., Ladjailia, A., Ejbali, R., & Zaied, M. (2019). Stacked sparse autoencoder and history of binary motion image for human activity recognition. Multimedia Tools and Applications, 78(2), 2157\u20132179. https:\/\/doi.org\/10.1007\/s11042-018-6273-1.","journal-title":"Multimedia Tools and Applications"},{"issue":"2","key":"758_CR17","doi-asserted-by":"publisher","first-page":"506","DOI":"10.1007\/s12555-015-0196-7","volume":"15","author":"M Hamadache","year":"2017","unstructured":"Hamadache, M., & Lee, D. (2017). Principal component analysis based signal-to-noise ratio improvement for inchoate faulty signals: Application to ball bearing fault detection. International Journal of Control, Automation and Systems, 15(2), 506\u2013517. https:\/\/doi.org\/10.1007\/s12555-015-0196-7.","journal-title":"International Journal of Control, Automation and Systems"},{"key":"758_CR18","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.cageo.2018.12.005","volume":"124","author":"T Horrocks","year":"2019","unstructured":"Horrocks, T., Holden, E. J., Wedge, D., Wijns, C., & Fiorentini, M. (2019). Geochemical characterisation of rock hydration processes using t-SNE. Computers & Geosciences, 124, 46\u201357. https:\/\/doi.org\/10.1016\/j.cageo.2018.12.005.","journal-title":"Computers & Geosciences"},{"key":"758_CR19","doi-asserted-by":"publisher","unstructured":"Husnain, M., Missen, M. M. S., Mumtaz, S., Luqman, M. M., Coustaty, M., & Ogier, J. M. (2019). Visualization of high-dimensional data by pairwise fusion matrices using t-SNE. Symmetry-Basel, 11(1). https:\/\/doi.org\/10.3390\/sym11010107.","DOI":"10.3390\/sym11010107"},{"issue":"5","key":"758_CR20","first-page":"569","volume":"25","author":"D Kim","year":"2018","unstructured":"Kim, D., Park, S. H., & Baek, J. G. (2018). A Kernel fisher discriminant analysis-based tree ensemble classifier: Kfda forest. International Journal of Industrial Engineering-Theory Applications and Practice, 25(5), 569\u2013579.","journal-title":"International Journal of Industrial Engineering-Theory Applications and Practice"},{"key":"758_CR21","doi-asserted-by":"publisher","first-page":"1180","DOI":"10.1016\/j.scitotenv.2018.04.361","volume":"636","author":"ZL Li","year":"2018","unstructured":"Li, Z. L., Bagan, H., & Yamagata, Y. (2018). Analysis of spatiotemporal land cover changes in Inner Mongolia using self-organizing map neural network and grid cells method. Science of the Total Environment, 636, 1180\u20131191. https:\/\/doi.org\/10.1016\/j.scitotenv.2018.04.361.","journal-title":"Science of the Total Environment"},{"key":"758_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2019.106971","author":"GC Liu","year":"2019","unstructured":"Liu, G. C., Li, L. L., Jiao, L. C., Dong, Y. S., & Li, X. L. (2019). Stacked Fisher autoencoder for SAR change detection. Pattern Recognition. https:\/\/doi.org\/10.1016\/j.patcog.2019.106971.","journal-title":"Pattern Recognition"},{"key":"758_CR23","doi-asserted-by":"publisher","unstructured":"Liukkonen, M., Hiltunen, Y., & Laakso, I. (2013). Advanced monitoring and diagnosis of industrial processes. In 2013 8th Eurosim congress on modelling and simulation (eurosim), 112\u2013117. https:\/\/doi.org\/10.1109\/eurosim.2013.30.","DOI":"10.1109\/eurosim.2013.30"},{"issue":"1","key":"758_CR24","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1080\/01431161.2018.1488291","volume":"40","author":"JW Ma","year":"2019","unstructured":"Ma, J. W., Nguyen, C. H., Lee, K., & Heo, J. (2019). Regional-scale rice-yield estimation using stacked auto-encoder with climatic and MODIS data: a case study of South Korea. International Journal of Remote Sensing, 40(1), 51\u201371. https:\/\/doi.org\/10.1080\/01431161.2018.1488291.","journal-title":"International Journal of Remote Sensing"},{"key":"758_CR25","unstructured":"Maaten, L. V. D. (2009). Learning a parametric embedding by preserving local structure. In International conference on artificial intelligence and statistics, 384\u2013391."},{"key":"758_CR26","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1016\/j.eswa.2018.10.012","volume":"119","author":"R Moradi","year":"2019","unstructured":"Moradi, R., Berangi, R., & Minaei, B. (2019). SparseMaps: Convolutional networks with sparse feature maps for tiny image classification. Expert Systems with Applications, 119, 142\u2013154. https:\/\/doi.org\/10.1016\/j.eswa.2018.10.012.","journal-title":"Expert Systems with Applications"},{"issue":"3","key":"758_CR27","doi-asserted-by":"publisher","first-page":"992","DOI":"10.1002\/aic.16497","volume":"65","author":"M Onel","year":"2019","unstructured":"Onel, M., Kieslich, C. A., & Pistikopoulos, E. N. (2019). A nonlinear support vector machine-based feature selection approach for fault detection and diagnosis: Application to the Tennessee Eastman process. AIChE Journal, 65(3), 992\u20131005. https:\/\/doi.org\/10.1002\/aic.16497.","journal-title":"AIChE Journal"},{"key":"758_CR28","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1016\/j.eswa.2018.05.016","volume":"114","author":"M Papakostas","year":"2018","unstructured":"Papakostas, M., & Giannakopoulos, T. (2018). Speech-music discrimination using deep visual feature extractors. Expert Systems with Applications, 114, 334\u2013344. https:\/\/doi.org\/10.1016\/j.eswa.2018.05.016.","journal-title":"Expert Systems with Applications"},{"key":"758_CR29","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1016\/j.chemolab.2019.03.012","volume":"189","author":"M Quinones-Grueiro","year":"2019","unstructured":"Quinones-Grueiro, M., Prieto-Moreno, A., Verde, C., & Llanes-Santiago, O. (2019). Data-driven monitoring of multimode continuous processes: A review. Chemometrics and Intelligent Laboratory Systems, 189, 56\u201371. https:\/\/doi.org\/10.1016\/j.chemolab.2019.03.012.","journal-title":"Chemometrics and Intelligent Laboratory Systems"},{"key":"758_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.compchemeng.2015.02.002","volume":"76","author":"G Robertson","year":"2015","unstructured":"Robertson, G., Thomas, M. C., & Romagnoli, J. A. (2015). Topological preservation techniques for nonlinear process monitoring. Computers & Chemical Engineering, 76, 1\u201316. https:\/\/doi.org\/10.1016\/j.compchemeng.2015.02.002.","journal-title":"Computers & Chemical Engineering"},{"key":"758_CR31","doi-asserted-by":"crossref","unstructured":"Shahriari, A. (2016). Learning of separable filters by stacked fisher convolutional autoencoders. Paper presented at the proceedings of the British Machine vision conference 2016.","DOI":"10.5244\/C.30.54"},{"issue":"1\u20132","key":"758_CR32","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1016\/j.ijpharm.2017.01.052","volume":"520","author":"AFT Silva","year":"2017","unstructured":"Silva, A. F. T., Sarraguca, M. C., Ribeiro, P. R., Santos, A. O., De Beer, T., & Lopes, J. A. (2017). Statistical process control of cocrystallization processes: A comparison between OPLS and PLS. International Journal of Pharmaceutics, 520(1\u20132), 29\u201338. https:\/\/doi.org\/10.1016\/j.ijpharm.2017.01.052.","journal-title":"International Journal of Pharmaceutics"},{"issue":"1","key":"758_CR33","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1252\/jcej.13we134","volume":"47","author":"Y Song","year":"2014","unstructured":"Song, Y., Jiang, Q. C., Yan, X. F., & Guo, M. J. (2014). A multi-SOM with canonical variate analysis for chemical process monitoring and fault diagnosis. Journal of Chemical Engineering of Japan, 47(1), 40\u201351. https:\/\/doi.org\/10.1252\/jcej.13we134.","journal-title":"Journal of Chemical Engineering of Japan"},{"key":"758_CR34","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1016\/j.asoc.2017.07.022","volume":"60","author":"JW Tang","year":"2017","unstructured":"Tang, J. W., & Yan, X. F. (2017). Neural network modeling relationship between inputs and state mapping plane obtained by FDA-t-SNE for visual industrial process monitoring. Applied Soft Computing, 60, 577\u2013590. https:\/\/doi.org\/10.1016\/j.asoc.2017.07.022.","journal-title":"Applied Soft Computing"},{"key":"758_CR35","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1016\/j.jpdc.2017.06.007","volume":"117","author":"C Tong","year":"2018","unstructured":"Tong, C., Li, J., Lang, C., Kong, F. X., Niu, J. W., & Rodrigues, J. J. P. C. (2018). An efficient deep model for day-ahead electricity load forecasting with stacked denoising auto-encoders. Journal of Parallel and Distributed Computing, 117, 267\u2013273. https:\/\/doi.org\/10.1016\/j.jpdc.2017.06.007.","journal-title":"Journal of Parallel and Distributed Computing"},{"key":"758_CR36","doi-asserted-by":"publisher","first-page":"474","DOI":"10.1016\/j.scitotenv.2016.11.071","volume":"579","author":"WP Tsai","year":"2017","unstructured":"Tsai, W. P., Huang, S. P., Cheng, S. T., Shao, K. T., & Chang, F. J. (2017). A data-mining framework for exploring the multi-relation between fish species and water quality through self-organizing map. Science of the Total Environment, 579, 474\u2013483. https:\/\/doi.org\/10.1016\/j.scitotenv.2016.11.071.","journal-title":"Science of the Total Environment"},{"key":"758_CR37","doi-asserted-by":"publisher","first-page":"763","DOI":"10.1016\/j.asoc.2017.03.011","volume":"60","author":"MA Valle","year":"2017","unstructured":"Valle, M. A., Ruz, G. A., & Masias, V. H. (2017). Using self-organizing maps to model turnover of sales agents in a call center. Applied Soft Computing, 60, 763\u2013774. https:\/\/doi.org\/10.1016\/j.asoc.2017.03.011.","journal-title":"Applied Soft Computing"},{"issue":"5","key":"758_CR38","doi-asserted-by":"publisher","first-page":"637","DOI":"10.1049\/iet-smt.2016.0391","volume":"11","author":"BX Wang","year":"2017","unstructured":"Wang, B. X., Pan, H. X., & Yang, W. (2017). Robust bearing degradation assessment method based on improved CVA. IET Science, Measurement and Technology, 11(5), 637\u2013645. https:\/\/doi.org\/10.1049\/iet-smt.2016.0391.","journal-title":"IET Science, Measurement and Technology"},{"issue":"21","key":"758_CR39","doi-asserted-by":"publisher","first-page":"8831","DOI":"10.1021\/ie500815a","volume":"53","author":"HY Yu","year":"2014","unstructured":"Yu, H. Y., Khan, F., Garaniya, V., & Ahmad, A. (2014). Self-organizing map based fault diagnosis technique for non-gaussian processes. Industrial and Engineering Chemistry Research, 53(21), 8831\u20138843. https:\/\/doi.org\/10.1021\/ie500815a.","journal-title":"Industrial and Engineering Chemistry Research"},{"issue":"45","key":"758_CR40","doi-asserted-by":"publisher","first-page":"15479","DOI":"10.1021\/acs.iecr.8b04689","volume":"57","author":"JB Yu","year":"2018","unstructured":"Yu, J. B., & Yan, X. F. (2018). Layer-by-layer enhancement strategy of favorable features of the deep belief network for industrial process monitoring. Industrial and Engineering Chemistry Research, 57(45), 15479\u201315490. https:\/\/doi.org\/10.1021\/acs.iecr.8b04689.","journal-title":"Industrial and Engineering Chemistry Research"},{"issue":"7","key":"758_CR41","doi-asserted-by":"publisher","first-page":"4823","DOI":"10.1109\/Tgrs.2019.2893180","volume":"57","author":"PC Zhou","year":"2019","unstructured":"Zhou, P. C., Han, J. W., Cheng, G., & Zhang, B. C. (2019). Learning compact and discriminative stacked autoencoder for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 57(7), 4823\u20134833. https:\/\/doi.org\/10.1109\/Tgrs.2019.2893180.","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"}],"container-title":["Multidimensional Systems and Signal Processing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11045-020-00758-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11045-020-00758-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11045-020-00758-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,3,17]],"date-time":"2021-03-17T19:25:10Z","timestamp":1616009110000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11045-020-00758-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,18]]},"references-count":41,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2021,4]]}},"alternative-id":["758"],"URL":"https:\/\/doi.org\/10.1007\/s11045-020-00758-5","relation":{},"ISSN":["0923-6082","1573-0824"],"issn-type":[{"type":"print","value":"0923-6082"},{"type":"electronic","value":"1573-0824"}],"subject":[],"published":{"date-parts":[[2021,1,18]]},"assertion":[{"value":"18 November 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 December 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 December 2020","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 January 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}