{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,29]],"date-time":"2024-08-29T00:23:07Z","timestamp":1724890987215},"reference-count":88,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems with Applications"],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1016\/j.eswa.2024.124725","type":"journal-article","created":{"date-parts":[[2024,7,10]],"date-time":"2024-07-10T18:19:12Z","timestamp":1720635552000},"page":"124725","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PC","title":["Kernelized Bures metric: A framework for effective domain adaptation in sensor data analysis"],"prefix":"10.1016","volume":"255","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-9562-8129","authenticated-orcid":false,"given":"Obsa","family":"Gilo","sequence":"first","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0001-8247-9040","authenticated-orcid":false,"given":"Jimson","family":"Mathew","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-2159-3410","authenticated-orcid":false,"given":"Samrat","family":"Mondal","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"year":"2014","author":"Ajakan","series-title":"Domain-adversarial neural networks","key":"10.1016\/j.eswa.2024.124725_b1"},{"issue":"1","key":"10.1016\/j.eswa.2024.124725_b2","doi-asserted-by":"crossref","first-page":"109","DOI":"10.3390\/s20010109","article-title":"Tackling faults in the industry 4.0 era\u2014A survey of machine-learning solutions and key aspects","volume":"20","author":"Angelopoulos","year":"2019","journal-title":"Sensors"},{"key":"10.1016\/j.eswa.2024.124725_b3","first-page":"3","article-title":"A public domain dataset for human activity recognition using smartphones","volume":"vol. 3","author":"Anguita","year":"2013"},{"key":"10.1016\/j.eswa.2024.124725_b4","series-title":"Proceedings of the 2005 IEEE international symposium on, mediterrean conference on control and automation intelligent control, 2005","first-page":"719","article-title":"A survey of applications of wireless sensors and wireless sensor networks","author":"Arampatzis","year":"2005"},{"issue":"4","key":"10.1016\/j.eswa.2024.124725_b5","doi-asserted-by":"crossref","first-page":"1377","DOI":"10.3390\/s22041377","article-title":"FL-PMI: Federated learning-based person movement identification through wearable devices in smart healthcare systems","volume":"22","author":"Arikumar","year":"2022","journal-title":"Sensors"},{"key":"10.1016\/j.eswa.2024.124725_b6","series-title":"2013 international conference on computer medical applications","first-page":"1","article-title":"Domain adaptation methods for ECG classification","author":"Bazi","year":"2013"},{"key":"10.1016\/j.eswa.2024.124725_b7","article-title":"Analysis of representations for domain adaptation","volume":"vol. 19","author":"Ben-David","year":"2006"},{"issue":"8","key":"10.1016\/j.eswa.2024.124725_b8","doi-asserted-by":"crossref","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","article-title":"Representation learning: A review and new perspectives","volume":"35","author":"Bengio","year":"2013","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"2","key":"10.1016\/j.eswa.2024.124725_b9","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.exmath.2018.01.002","article-title":"On the Bures\u2013Wasserstein distance between positive definite matrices","volume":"37","author":"Bhatia","year":"2019","journal-title":"Expositiones Mathematicae"},{"key":"10.1016\/j.eswa.2024.124725_b10","first-page":"6859","article-title":"Time series domain adaptation via sparse associative structure alignment","volume":"vol. 35","author":"Cai","year":"2021"},{"issue":"1","key":"10.1016\/j.eswa.2024.124725_b11","doi-asserted-by":"crossref","first-page":"6085","DOI":"10.1038\/s41598-018-24271-9","article-title":"Recurrent neural networks for multivariate time series with missing values","volume":"8","author":"Che","year":"2018","journal-title":"Scientific Reports"},{"key":"10.1016\/j.eswa.2024.124725_b12","first-page":"3296","article-title":"Joint domain alignment and discriminative feature learning for unsupervised deep domain adaptation","volume":"vol. 33","author":"Chen","year":"2019"},{"key":"10.1016\/j.eswa.2024.124725_b13","first-page":"3422","article-title":"Homm: Higher-order moment matching for unsupervised domain adaptation","volume":"vol. 34","author":"Chen","year":"2020"},{"key":"10.1016\/j.eswa.2024.124725_b14","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2020.107463","article-title":"Covariance descriptors on a gaussian manifold and their application to image set classification","volume":"107","author":"Chen","year":"2020","journal-title":"Pattern Recognition"},{"key":"10.1016\/j.eswa.2024.124725_b15","series-title":"International conference on machine learning","first-page":"1081","article-title":"Transferability vs. discriminability: Batch spectral penalization for adversarial domain adaptation","author":"Chen","year":"2019"},{"year":"2019","author":"Choi","series-title":"Pseudo-labeling curriculum for unsupervised domain adaptation","key":"10.1016\/j.eswa.2024.124725_b16"},{"key":"10.1016\/j.eswa.2024.124725_b17","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2019.106682","article-title":"Remaining useful lifetime prediction via deep domain adaptation","volume":"195","author":"da Costa","year":"2020","journal-title":"Reliability Engineering & System Safety"},{"key":"10.1016\/j.eswa.2024.124725_b18","article-title":"Joint distribution optimal transportation for domain adaptation","volume":"30","author":"Courty","year":"2017","journal-title":"Advances in Neural Information Processing Systems"},{"doi-asserted-by":"crossref","unstructured":"Damodaran, B. B., Kellenberger, B., Flamary, R., Tuia, D., & Courty, N. (2018). Deepjdot: Deep joint distribution optimal transport for unsupervised domain adaptation. In Proceedings of the European conference on computer vision (pp. 447\u2013463).","key":"10.1016\/j.eswa.2024.124725_b19","DOI":"10.1007\/978-3-030-01225-0_28"},{"key":"10.1016\/j.eswa.2024.124725_b20","series-title":"Joint European conference on machine learning and knowledge discovery in databases","first-page":"52","article-title":"The bures metric for generative adversarial networks","author":"De Meulemeester","year":"2021"},{"issue":"3","key":"10.1016\/j.eswa.2024.124725_b21","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1016\/0047-259X(82)90077-X","article-title":"The Fr\u00e9chet distance between multivariate normal distributions","volume":"12","author":"Dowson","year":"1982","journal-title":"Journal of Multivariate Analysis"},{"key":"10.1016\/j.eswa.2024.124725_b22","article-title":"Contrastive domain adaptation for time-series via temporal mixup","author":"Eldele","year":"2023","journal-title":"IEEE Transactions on Artificial Intelligence"},{"year":"2021","author":"Eldele","series-title":"Time-series representation learning via temporal and contextual contrasting","key":"10.1016\/j.eswa.2024.124725_b23"},{"key":"10.1016\/j.eswa.2024.124725_b24","series-title":"International conference on machine learning","first-page":"1180","article-title":"Unsupervised domain adaptation by backpropagation","author":"Ganin","year":"2015"},{"issue":"1","key":"10.1016\/j.eswa.2024.124725_b25","first-page":"2030","article-title":"Domain-adversarial training of neural networks","volume":"17","author":"Ganin","year":"2016","journal-title":"The Journal of Machine Learning Research"},{"issue":"1","key":"10.1016\/j.eswa.2024.124725_b26","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1002\/mana.19901470121","article-title":"On a formula for the L2 Wasserstein metric between measures on Euclidean and Hilbert spaces","volume":"147","author":"Gelbrich","year":"1990","journal-title":"Mathematische Nachrichten"},{"issue":"23","key":"10.1016\/j.eswa.2024.124725_b27","doi-asserted-by":"crossref","first-page":"e215","DOI":"10.1161\/01.CIR.101.23.e215","article-title":"PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals","volume":"101","author":"Goldberger","year":"2000","journal-title":"Circulation"},{"issue":"1","key":"10.1016\/j.eswa.2024.124725_b28","first-page":"723","article-title":"A kernel two-sample test","volume":"13","author":"Gretton","year":"2012","journal-title":"Journal of Machine Learning Research"},{"unstructured":"Gu, Q., Li, Z., & Han, J. (2011). Joint feature selection and subspace learning. In Twenty-second international joint conference on artificial intelligence.","key":"10.1016\/j.eswa.2024.124725_b29"},{"key":"10.1016\/j.eswa.2024.124725_b30","article-title":"Log-Hilbert-Schmidt metric between positive definite operators on Hilbert spaces","volume":"vol. 27","author":"Ha Quang","year":"2014"},{"issue":"1","key":"10.1016\/j.eswa.2024.124725_b31","doi-asserted-by":"crossref","first-page":"56","DOI":"10.3390\/s22010056","article-title":"Bearing fault diagnosis using multidomain fusion-based vibration imaging and multitask learning","volume":"22","author":"Hasan","year":"2021","journal-title":"Sensors"},{"year":"2023","author":"He","series-title":"Domain adaptation for time series under feature and label shifts","key":"10.1016\/j.eswa.2024.124725_b32"},{"issue":"2","key":"10.1016\/j.eswa.2024.124725_b33","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1039\/C7LC00914C","article-title":"Wearable sensors: Modalities, challenges, and prospects","volume":"18","author":"Heikenfeld","year":"2018","journal-title":"Lab on a Chip"},{"issue":"1","key":"10.1016\/j.eswa.2024.124725_b34","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1007\/s11263-014-0719-3","article-title":"Asymmetric and category invariant feature transformations for domain adaptation","volume":"109","author":"Hoffman","year":"2014","journal-title":"International Journal of Computer Vision"},{"year":"2018","author":"Hosseini-Asl","series-title":"Augmented cyclic adversarial learning for low resource domain adaptation","key":"10.1016\/j.eswa.2024.124725_b35"},{"key":"10.1016\/j.eswa.2024.124725_b36","article-title":"Correcting sample selection bias by unlabeled data","volume":"vol. 19","author":"Huang","year":"2006"},{"key":"10.1016\/j.eswa.2024.124725_b37","doi-asserted-by":"crossref","DOI":"10.1016\/j.sintl.2021.100121","article-title":"Sensors for daily life: A review","volume":"2","author":"Javaid","year":"2021","journal-title":"Sensors International"},{"key":"10.1016\/j.eswa.2024.124725_b38","series-title":"International conference on machine learning","first-page":"10280","article-title":"Domain adaptation for time series forecasting via attention sharing","author":"Jin","year":"2022"},{"issue":"23","key":"10.1016\/j.eswa.2024.124725_b39","doi-asserted-by":"crossref","first-page":"6783","DOI":"10.3390\/s20236783","article-title":"Advances in sensor technologies in the era of smart factory and industry 4.0","volume":"20","author":"Kalsoom","year":"2020","journal-title":"Sensors"},{"key":"10.1016\/j.eswa.2024.124725_b40","series-title":"International conference on machine learning","first-page":"730","article-title":"Asymmetric transfer learning with deep gaussian processes","author":"Kandemir","year":"2015"},{"doi-asserted-by":"crossref","unstructured":"Kang, G., Jiang, L., Yang, Y., & Hauptmann, A. G. (2019). Contrastive adaptation network for unsupervised domain adaptation. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 4893\u20134902).","key":"10.1016\/j.eswa.2024.124725_b41","DOI":"10.1109\/CVPR.2019.00503"},{"year":"2014","author":"Kingma","series-title":"Adam: A method for stochastic optimization","key":"10.1016\/j.eswa.2024.124725_b42"},{"key":"10.1016\/j.eswa.2024.124725_b43","series-title":"2018 26th telecommunications forum","first-page":"420","article-title":"Sensors and sensor fusion in autonomous vehicles","author":"Koci\u0107","year":"2018"},{"issue":"1","key":"10.1016\/j.eswa.2024.124725_b44","first-page":"5943","article-title":"Feature-level domain adaptation","volume":"17","author":"Kouw","year":"2016","journal-title":"Journal of Machine Learning Research"},{"issue":"2","key":"10.1016\/j.eswa.2024.124725_b45","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1145\/1964897.1964918","article-title":"Activity recognition using cell phone accelerometers","volume":"12","author":"Kwapisz","year":"2011","journal-title":"ACM SigKDD Explorations Newsletter"},{"issue":"5","key":"10.1016\/j.eswa.2024.124725_b46","doi-asserted-by":"crossref","first-page":"1383","DOI":"10.3390\/s18051383","article-title":"Electroencephalography based fusion two-dimensional (2D)-Convolution Neural Networks (CNN) model for emotion recognition system","volume":"18","author":"Kwon","year":"2018","journal-title":"Sensors"},{"issue":"14","key":"10.1016\/j.eswa.2024.124725_b47","doi-asserted-by":"crossref","first-page":"3156","DOI":"10.3390\/s19143156","article-title":"Polymer optical fiber sensors in healthcare applications: A comprehensive review","volume":"19","author":"Leal-Junior","year":"2019","journal-title":"Sensors"},{"doi-asserted-by":"crossref","unstructured":"Liu, Q., & Xue, H. (2021). Adversarial Spectral Kernel Matching for Unsupervised Time Series Domain Adaptation. In IJCAI (pp. 2744\u20132750).","key":"10.1016\/j.eswa.2024.124725_b48","DOI":"10.24963\/ijcai.2021\/378"},{"issue":"6","key":"10.1016\/j.eswa.2024.124725_b49","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1016\/j.pmcj.2009.07.007","article-title":"uWave: Accelerometer-based personalized gesture recognition and its applications","volume":"5","author":"Liu","year":"2009","journal-title":"Pervasive and Mobile Computing"},{"key":"10.1016\/j.eswa.2024.124725_b50","series-title":"International conference on machine learning","first-page":"97","article-title":"Learning transferable features with deep adaptation networks","author":"Long","year":"2015"},{"key":"10.1016\/j.eswa.2024.124725_b51","article-title":"Conditional adversarial domain adaptation","volume":"vol. 31","author":"Long","year":"2018"},{"doi-asserted-by":"crossref","unstructured":"Long, M., Wang, J., Ding, G., Sun, J., & Yu, P. S. (2014). Transfer joint matching for unsupervised domain adaptation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1410\u20131417).","key":"10.1016\/j.eswa.2024.124725_b52","DOI":"10.1109\/CVPR.2014.183"},{"doi-asserted-by":"crossref","unstructured":"Luo, Y.-W., & Ren, C.-X. (2021). Conditional bures metric for domain adaptation. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 13989\u201313998).","key":"10.1016\/j.eswa.2024.124725_b53","DOI":"10.1109\/CVPR46437.2021.01377"},{"year":"2022","author":"Ozyurt","series-title":"Contrastive learning for unsupervised domain adaptation of time series","key":"10.1016\/j.eswa.2024.124725_b54"},{"doi-asserted-by":"crossref","unstructured":"Pei, Z., Cao, Z., Long, M., & Wang, J. (2018). Multi-adversarial domain adaptation. In Thirty-second AAAI conference on artificial intelligence.","key":"10.1016\/j.eswa.2024.124725_b55","DOI":"10.1609\/aaai.v32i1.11767"},{"key":"10.1016\/j.eswa.2024.124725_b56","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1007\/s11263-005-3222-z","article-title":"A Riemannian framework for tensor computing","volume":"66","author":"Pennec","year":"2006","journal-title":"International Journal of Computer Vision"},{"issue":"5\u20136","key":"10.1016\/j.eswa.2024.124725_b57","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1561\/2200000073","article-title":"Computational optimal transport: With applications to data science","volume":"11","author":"Peyr\u00e9","year":"2019","journal-title":"Foundations and Trends\u00ae in Machine Learning"},{"unstructured":"Purushotham, S., Carvalho, W., Nilanon, T., & Liu, Y. (2016). Variational recurrent adversarial deep domain adaptation. In International conference on learning representations.","key":"10.1016\/j.eswa.2024.124725_b58"},{"year":"2021","author":"Ragab","series-title":"A systematic evaluation of domain adaptation algorithms on time series data","key":"10.1016\/j.eswa.2024.124725_b59"},{"issue":"8","key":"10.1016\/j.eswa.2024.124725_b60","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3587937","article-title":"Adatime: A benchmarking suite for domain adaptation on time series data","volume":"17","author":"Ragab","year":"2023","journal-title":"ACM Transactions on Knowledge Discovery from Data"},{"year":"2022","author":"Roelofs","series-title":"AdaMatch: A unified approach to semi-supervised learning and domain adaptation","key":"10.1016\/j.eswa.2024.124725_b61"},{"year":"2016","author":"Ruder","series-title":"An overview of gradient descent optimization algorithms","key":"10.1016\/j.eswa.2024.124725_b62"},{"key":"10.1016\/j.eswa.2024.124725_b63","series-title":"European conference on computer vision","first-page":"213","article-title":"Adapting visual category models to new domains","author":"Saenko","year":"2010"},{"issue":"15","key":"10.1016\/j.eswa.2024.124725_b64","doi-asserted-by":"crossref","first-page":"5507","DOI":"10.3390\/s22155507","article-title":"Deep unsupervised domain adaptation with time series sensor data: A survey","volume":"22","author":"Shi","year":"2022","journal-title":"Sensors"},{"year":"2018","author":"Shu","series-title":"A dirt-t approach to unsupervised domain adaptation","key":"10.1016\/j.eswa.2024.124725_b65"},{"doi-asserted-by":"crossref","unstructured":"Stisen, A., Blunck, H., Bhattacharya, S., Prentow, T. S., Kj\u00e6rgaard, M. B., Dey, A., et al. (2015). Smart devices are different: Assessing and mitigatingmobile sensing heterogeneities for activity recognition. In Proceedings of the 13th ACM conference on embedded networked sensor systems (pp. 127\u2013140).","key":"10.1016\/j.eswa.2024.124725_b66","DOI":"10.1145\/2809695.2809718"},{"key":"10.1016\/j.eswa.2024.124725_b67","article-title":"Return of frustratingly easy domain adaptation","volume":"vol. 30","author":"Sun","year":"2016"},{"key":"10.1016\/j.eswa.2024.124725_b68","series-title":"Domain adaptation in computer vision applications","first-page":"153","article-title":"Correlation alignment for unsupervised domain adaptation","author":"Sun","year":"2017"},{"key":"10.1016\/j.eswa.2024.124725_b69","series-title":"European conference on computer vision","first-page":"443","article-title":"Deep coral: Correlation alignment for deep domain adaptation","author":"Sun","year":"2016"},{"key":"10.1016\/j.eswa.2024.124725_b70","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.robot.2019.02.007","article-title":"Robust and subject-independent driving manoeuvre anticipation through domain-adversarial recurrent neural networks","volume":"115","author":"Tonutti","year":"2019","journal-title":"Robotics and Autonomous Systems"},{"issue":"10","key":"10.1016\/j.eswa.2024.124725_b71","doi-asserted-by":"crossref","first-page":"1713","DOI":"10.1109\/TPAMI.2008.75","article-title":"Pedestrian detection via classification on riemannian manifolds","volume":"30","author":"Tuzel","year":"2008","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"doi-asserted-by":"crossref","unstructured":"Tzeng, E., Hoffman, J., Saenko, K., & Darrell, T. (2017). Adversarial discriminative domain adaptation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 7167\u20137176).","key":"10.1016\/j.eswa.2024.124725_b72","DOI":"10.1109\/CVPR.2017.316"},{"key":"10.1016\/j.eswa.2024.124725_b73","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1016\/j.trc.2018.02.012","article-title":"Autonomous vehicle perception: The technology of today and tomorrow","volume":"89","author":"Van Brummelen","year":"2018","journal-title":"Transportation Research Part C: Emerging Technologies"},{"issue":"11","key":"10.1016\/j.eswa.2024.124725_b74","article-title":"Visualizing data using t-SNE","volume":"9","author":"Van der Maaten","year":"2008","journal-title":"Journal of Machine Learning Research"},{"key":"10.1016\/j.eswa.2024.124725_b75","first-page":"6243","article-title":"Unsupervised domain adaptation via structured prediction based selective pseudo-labeling","volume":"vol. 34","author":"Wang","year":"2020"},{"key":"10.1016\/j.eswa.2024.124725_b76","series-title":"2012 IEEE conference on computer vision and pattern recognition","first-page":"2496","article-title":"Covariance discriminative learning: A natural and efficient approach to image set classification","author":"Wang","year":"2012"},{"issue":"8","key":"10.1016\/j.eswa.2024.124725_b77","doi-asserted-by":"crossref","first-page":"8430","DOI":"10.1109\/TIE.2021.3108726","article-title":"Subdomain adaptation transfer learning network for fault diagnosis of roller bearings","volume":"69","author":"Wang","year":"2021","journal-title":"IEEE Transactions on Industrial Electronics"},{"key":"10.1016\/j.eswa.2024.124725_b78","article-title":"Rethinking maximum mean discrepancy for visual domain adaptation","author":"Wang","year":"2021","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"year":"2020","author":"Wang","series-title":"Rethink maximum mean discrepancy for domain adaptation","key":"10.1016\/j.eswa.2024.124725_b79"},{"key":"10.1016\/j.eswa.2024.124725_b80","article-title":"Subdomain adaptation with manifolds discrepancy alignment","author":"Wei","year":"2021","journal-title":"IEEE Transactions on Cybernetics"},{"doi-asserted-by":"crossref","unstructured":"Wilson, G., Doppa, J. R., & Cook, D. J. (2020). Multi-source deep domain adaptation with weak supervision for time-series sensor data. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining (pp. 1768\u20131778).","key":"10.1016\/j.eswa.2024.124725_b81","DOI":"10.1145\/3394486.3403228"},{"key":"10.1016\/j.eswa.2024.124725_b82","article-title":"Controllable invariance through adversarial feature learning","volume":"vol. 30","author":"Xie","year":"2017"},{"issue":"3","key":"10.1016\/j.eswa.2024.124725_b83","doi-asserted-by":"crossref","first-page":"1273","DOI":"10.1109\/JBHI.2021.3103614","article-title":"Transferring structured knowledge in unsupervised domain adaptation of a sleep staging network","volume":"26","author":"Yoo","year":"2021","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"issue":"7","key":"10.1016\/j.eswa.2024.124725_b84","doi-asserted-by":"crossref","first-page":"1741","DOI":"10.1109\/TPAMI.2019.2903050","article-title":"Optimal transport in reproducing kernel hilbert spaces: Theory and applications","volume":"42","author":"Zhang","year":"2019","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"12","key":"10.1016\/j.eswa.2024.124725_b85","doi-asserted-by":"crossref","first-page":"1330","DOI":"10.1038\/s41551-022-00898-y","article-title":"Shifting machine learning for healthcare from development to deployment and from models to data","volume":"6","author":"Zhang","year":"2022","journal-title":"Nature Biomedical Engineering"},{"key":"10.1016\/j.eswa.2024.124725_b86","first-page":"3988","article-title":"Self-supervised contrastive pre-training for time series via time-frequency consistency","volume":"35","author":"Zhang","year":"2022","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.eswa.2024.124725_b87","series-title":"International Conference on Machine Learning","first-page":"4100","article-title":"Learning sleep stages from radio signals: A conditional adversarial architecture","author":"Zhao","year":"2017"},{"issue":"4","key":"10.1016\/j.eswa.2024.124725_b88","doi-asserted-by":"crossref","first-page":"1713","DOI":"10.1109\/TNNLS.2020.2988928","article-title":"Deep subdomain adaptation network for image classification","volume":"32","author":"Zhu","year":"2020","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"}],"container-title":["Expert Systems with Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417424015926?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417424015926?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,8,28]],"date-time":"2024-08-28T17:33:50Z","timestamp":1724866430000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0957417424015926"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12]]},"references-count":88,"alternative-id":["S0957417424015926"],"URL":"https:\/\/doi.org\/10.1016\/j.eswa.2024.124725","relation":{},"ISSN":["0957-4174"],"issn-type":[{"type":"print","value":"0957-4174"}],"subject":[],"published":{"date-parts":[[2024,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Kernelized Bures metric: A framework for effective domain adaptation in sensor data analysis","name":"articletitle","label":"Article Title"},{"value":"Expert Systems with Applications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.eswa.2024.124725","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"124725"}}