{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,9]],"date-time":"2025-06-09T22:26:52Z","timestamp":1749508012343},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2020,2,7]],"date-time":"2020-02-07T00:00:00Z","timestamp":1581033600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,2,7]],"date-time":"2020-02-07T00:00:00Z","timestamp":1581033600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2018YFB0803400"],"award-info":[{"award-number":["2018YFB0803400"]}]},{"name":"Nature Science Foundation of Jiangsu for Distinguished Young Scientist","award":["BK20170039"],"award-info":[{"award-number":["BK20170039"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61932013"],"award-info":[{"award-number":["61932013"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2020,6]]},"DOI":"10.1007\/s10489-019-01620-3","type":"journal-article","created":{"date-parts":[[2020,2,7]],"date-time":"2020-02-07T01:02:10Z","timestamp":1581037330000},"page":"1674-1686","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Online transferable representation with heterogeneous sources"],"prefix":"10.1007","volume":"50","author":[{"given":"Yanchao","family":"Li","sequence":"first","affiliation":[]},{"given":"Hao","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,2,7]]},"reference":[{"key":"1620_CR1","doi-asserted-by":"crossref","unstructured":"Bengio Y, Lamblin P, Popovici D, Larochelle H (2007) Greedy layer-wise training of deep networks. In: Advances in neural information processing systems, pp 153\u2013160","DOI":"10.7551\/mitpress\/7503.003.0024"},{"issue":"9","key":"1620_CR2","doi-asserted-by":"publisher","first-page":"2050","DOI":"10.1109\/TIT.2004.833339","volume":"50","author":"N Cesa-Bianchi","year":"2004","unstructured":"Cesa-Bianchi N, Conconi A, Gentile C (2004) On the generalization ability of on-line learning algorithms. IEEE Trans Inf Theory 50(9):2050\u20132057","journal-title":"IEEE Trans Inf Theory"},{"key":"1620_CR3","doi-asserted-by":"crossref","unstructured":"Chandra S, Haque A, Khan L, Aggarwal C (2016) An adaptive framework for multistream classification. In: ACM International on conference on information and knowledge management. ACM, pp 1181\u20131190","DOI":"10.1145\/2983323.2983842"},{"key":"1620_CR4","doi-asserted-by":"crossref","unstructured":"Chandra S, Haque A, Tao H, Liu J, Khan L, Aggarwal C (2018) Ensemble direct density ratio estimation for multistream classification. In: IEEE International conference on data engineering. IEEE, pp 1364\u20131367","DOI":"10.1109\/ICDE.2018.00151"},{"key":"1620_CR5","unstructured":"Chechik G, Shalit U, Sharma V, Bengio S (2009) An online algorithm for large scale image similarity learning. In: Advances in neural information processing systems, pp 306\u2013314"},{"key":"1620_CR6","unstructured":"Chen M, Xu Z, Weinberger K, Sha F (2012) Marginalized denoising autoencoders for domain adaptation. arXiv"},{"key":"1620_CR7","doi-asserted-by":"crossref","unstructured":"Dong B, Gao Y, Chandra S, Khan L (2019) Multistream classification with relative density ratio estimation. In: AAAI, vol 33, pp 3478\u20133485","DOI":"10.1609\/aaai.v33i01.33013478"},{"key":"1620_CR8","unstructured":"Gama J, Kosina P, et al. (2011) Learning decision rules from data streams. In: IJCAI, pp 1255\u20131262"},{"key":"1620_CR9","unstructured":"Gillen S, Jung C, Kearns M, Roth A (2018) Online learning with an unknown fairness metric. In: Advances in neural information processing systems, pp 2605\u20132614"},{"key":"1620_CR10","unstructured":"Glorot X, Bordes A, Bengio Y (2011) Domain adaptation for large-scale sentiment classification: a deep learning approach. In: International conference on machine learning, pp 513\u2013520"},{"issue":"1","key":"1620_CR11","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1109\/TNNLS.2013.2271915","volume":"25","author":"JB Gomes","year":"2014","unstructured":"Gomes JB, Gaber MM, Sousa PA, Menasalvas E (2014) Mining recurring concepts in a dynamic feature space. IEEE Trans Neural Netw Learn Syst 25(1):95\u2013110","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"1620_CR12","doi-asserted-by":"crossref","unstructured":"Haque A, Chandra S, Khan L, Hamlen K, Aggarwal C (2017) Efficient multistream classification using direct density ratio estimation. In: IEEE International conference on data engineering. IEEE, pp 155\u2013158","DOI":"10.1109\/ICDE.2017.63"},{"key":"1620_CR13","doi-asserted-by":"crossref","unstructured":"Haque A, Wang Z, Chandra S, Dong B, Khan L, Hamlen KW (2017) Fusion: an online method for multistream classification. In: ACM on conference on information and knowledge management. ACM, pp 919\u2013928","DOI":"10.1145\/3132847.3132886"},{"key":"1620_CR14","doi-asserted-by":"crossref","unstructured":"Larochelle H, Erhan D, Courville A, Bergstra J, Bengio Y (2007) An empirical evaluation of deep architectures on problems with many factors of variation. In: International conference on machine learning, pp 473\u2013480","DOI":"10.1145\/1273496.1273556"},{"key":"1620_CR15","first-page":"1909","volume":"7","author":"P Laskov","year":"2006","unstructured":"Laskov P, Gehl C, Kr\u00fcger S, M\u00fcller KR (2006) Incremental support vector learning: analysis, implementation and applications. J Mach Learn Res 7:1909\u20131936","journal-title":"J Mach Learn Res"},{"key":"1620_CR16","first-page":"200","volume":"152","author":"Y Li","year":"2018","unstructured":"Li Y, Wang Y, Liu Q, Bi C, Jiang X, Shurong S (2018) Incremental semi-supervised learning on streaming data. Pattern Recogn 152:200\u2013214","journal-title":"Pattern Recogn"},{"key":"1620_CR17","unstructured":"Li YF, Gao Y, Ayoade G, Tao H, Khan L, Thuraisingham B (2019) Multistream classification for cyber threat data with heterogeneous feature space. In: The World Wide Web conference. ACM, pp 2992\u20132998"},{"key":"1620_CR18","doi-asserted-by":"crossref","unstructured":"Luo Y, Liu T, Wen Y, Tao D (2018) Online heterogeneous transfer metric learning. In: IJCAI, pp 2525\u20132531","DOI":"10.24963\/ijcai.2018\/350"},{"key":"1620_CR19","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.neunet.2015.08.001","volume":"71","author":"S Mehrkanoon","year":"2015","unstructured":"Mehrkanoon S, Agudelo OM, Suykens JA (2015) Incremental multi-class semi-supervised clustering regularized by Kalman filtering. Neural Netw 71:88\u2013104","journal-title":"Neural Netw"},{"issue":"1","key":"1620_CR20","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1109\/TNNLS.2013.2271933","volume":"25","author":"M Pratama","year":"2014","unstructured":"Pratama M, Anavatti SG, Angelov PP, Lughofer E (2014) Panfis: a novel incremental learning machine. IEEE Trans Neural Netw Learn Syst 25(1):55\u201368","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"1620_CR21","unstructured":"Precup D, Pineau J, Barreto AS (2012) On-line reinforcement learning using incremental kernel-based stochastic factorization. In: Adv Neural Inform Process Syst, pp 1484\u20131492"},{"key":"1620_CR22","unstructured":"Rifai S, Vincent P, Muller X, Glorot X, Bengio Y (2011) Contractive auto-encoders: explicit invariance during feature extraction. In: International conference on machine learning, pp 833\u2013840"},{"key":"1620_CR23","doi-asserted-by":"crossref","unstructured":"van Rijn JN, Holmes G, Pfahringer B, Vanschoren J (2015) Having a blast: meta-learning and heterogeneous ensembles for data streams. In: IEEE International conference on data mining. IEEE, pp 1003\u20131008","DOI":"10.1109\/ICDM.2015.55"},{"issue":"1","key":"1620_CR24","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1007\/s10994-017-5686-9","volume":"107","author":"JN van Rijn","year":"2018","unstructured":"van Rijn JN, Holmes G, Pfahringer B, Vanschoren J (2018) The online performance estimation framework: heterogeneous ensemble learning for data streams. Mach Learn 107(1):149\u2013176","journal-title":"Mach Learn"},{"issue":"5","key":"1620_CR25","doi-asserted-by":"publisher","first-page":"615","DOI":"10.1109\/TKDE.2007.190727","volume":"20","author":"PP Rodrigues","year":"2008","unstructured":"Rodrigues PP, Gama J, Pedroso J (2008) Hierarchical clustering of time-series data streams. IEEE Trans Knowl Data Eng 20(5):615\u2013627","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1620_CR26","unstructured":"Rosenfeld A, Tsotsos JK (2018) Incremental learning through deep adaptation. IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"1620_CR27","doi-asserted-by":"crossref","unstructured":"Shi X, Liu Q, Fan W, Philip SY, Zhu R (2010) Transfer learning on heterogenous feature spaces via spectral transformation. In: IEEE International conference on data mining. IEEE, pp 1049\u20131054","DOI":"10.1109\/ICDM.2010.65"},{"key":"1620_CR28","unstructured":"Socher R, Ganjoo M, Manning CD, Ng A (2013) Zero-shot learning through cross-modal transfer. In: Advances in neural information processing systems, pp 935\u2013943"},{"key":"1620_CR29","unstructured":"Sonderby CK, Raiko T, Maaloe L, Sonderby SK, Winther O (2016) Ladder variational autoencoders. In: Advances in neural information processing systems, pp 3738\u20133746"},{"issue":"4s","key":"1620_CR30","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1145\/2998574","volume":"12","author":"J Tang","year":"2016","unstructured":"Tang J, Shu X, Li Z, Qi GJ, Wang J (2016) Generalized deep transfer networks for knowledge propagation in heterogeneous domains. ACM Trans Multimed Comput Commun Appl 12(4s):68","journal-title":"ACM Trans Multimed Comput Commun Appl"},{"key":"1620_CR31","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.patcog.2018.01.020","volume":"79","author":"Z Tu","year":"2018","unstructured":"Tu Z, Xie W, Qin Q, Poppe R, Veltkamp RC, Li B, Yuan J (2018) Multi-stream cnn: learning representations based on human-related regions for action recognition. Pattern Recogn 79:32\u201343","journal-title":"Pattern Recogn"},{"key":"1620_CR32","doi-asserted-by":"crossref","unstructured":"Vincent P, Larochelle H, Bengio Y, Manzagol PA (2008) Extracting and composing robust features with denoising autoencoders. In: International conference on machine learning, pp 1096\u20131103","DOI":"10.1145\/1390156.1390294"},{"key":"1620_CR33","doi-asserted-by":"crossref","unstructured":"Wang Y, Fan X, Luo Z, Wang T, Min M, Luo J (2017) Fast online incremental learning on mixture streaming data. In: AAAI, pp 2739\u20132745","DOI":"10.1609\/aaai.v31i1.10874"},{"issue":"3","key":"1620_CR34","first-page":"26","volume":"10","author":"H Wu","year":"2019","unstructured":"Wu H, Yan Y, Ye Y, Min H, Ng MK, Wu Q (2019) Online heterogeneous transfer learning by knowledge transition. ACM Trans Intell Syst Technol (TIST) 10(3):26","journal-title":"ACM Trans Intell Syst Technol (TIST)"},{"key":"1620_CR35","doi-asserted-by":"crossref","unstructured":"Wu Z, Jiang YG, Wang X, Ye H, Xue X (2016) Multi-stream multi-class fusion of deep networks for video classification. In: ACM International conference on multimedia. ACM, pp 791\u2013800","DOI":"10.1145\/2964284.2964328"},{"issue":"5","key":"1620_CR36","doi-asserted-by":"publisher","first-page":"936","DOI":"10.1007\/s12083-015-0354-y","volume":"9","author":"F Xiao","year":"2016","unstructured":"Xiao F, Xie X, Jiang Z, Sun L, Wang R (2016) Utility-aware data transmission scheme for delay tolerant networks. Peer-to-Peer Network Appl 9(5):936\u2013944","journal-title":"Peer-to-Peer Network Appl"},{"issue":"4","key":"1620_CR37","doi-asserted-by":"publisher","first-page":"397","DOI":"10.1109\/TST.2016.7536717","volume":"21","author":"F Xiao","year":"2016","unstructured":"Xiao F, Yang X, Yang M, Sun L, Wang R, Yang P (2016) Surface coverage algorithm in directional sensor networks for three-dimensional complex terrains. Tsinghua Sci Technol 21(4):397\u2013406","journal-title":"Tsinghua Sci Technol"},{"issue":"2","key":"1620_CR38","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1109\/TAFFC.2016.2622690","volume":"9","author":"B Xu","year":"2018","unstructured":"Xu B, Fu Y, Jiang YG, Li B, Sigal L (2018) Heterogeneous knowledge transfer in video emotion recognition, attribution and summarization. IEEE Trans Affect Comput 9(2):255\u2013270","journal-title":"IEEE Trans Affect Comput"},{"issue":"7","key":"1620_CR39","first-page":"3252","volume":"29","author":"Y Yan","year":"2018","unstructured":"Yan Y, Wu Q, Tan M, Ng MK, Min H, Tsang IW (2018) Online heterogeneous transfer by hedge ensemble of offline and online decisions. IEEE Trans Neural Netw Learn Syst 29(7):3252\u2013 3263","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"11","key":"1620_CR40","doi-asserted-by":"publisher","first-page":"3726","DOI":"10.1016\/j.patcog.2014.05.022","volume":"47","author":"XQ Zeng","year":"2014","unstructured":"Zeng XQ, Li GZ (2014) Incremental partial least squares analysis of big streaming data. Pattern Recogn 47(11):3726\u20133735","journal-title":"Pattern Recogn"},{"key":"1620_CR41","unstructured":"Zhang X, Yu FX, Chang SF, Wang S (2015) Deep transfer network: unsupervised domain adaptation. arXiv"},{"issue":"1s","key":"1620_CR42","first-page":"9","volume":"15","author":"L Zhao","year":"2019","unstructured":"Zhao L, Chen Z, Yang LT, Deen MJ, Wang ZJ (2019) Deep semantic mapping for heterogeneous multimedia transfer learning using co-occurrence data. ACM Trans Multimed Comput Commun Appl 15(1s):9","journal-title":"ACM Trans Multimed Comput Commun Appl"},{"key":"1620_CR43","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1016\/j.artint.2014.06.003","volume":"216","author":"P Zhao","year":"2014","unstructured":"Zhao P, Hoi SC, Wang J, Li B (2014) Online transfer learning. Artif Intell 216:76\u2013102","journal-title":"Artif Intell"},{"key":"1620_CR44","unstructured":"Zhou G, Sohn K, Lee H (2012) Online incremental feature learning with denoising autoencoders. In: International conference on artificial intelligence and statistics, pp 1453\u20131461"},{"key":"1620_CR45","doi-asserted-by":"crossref","unstructured":"Zhou JT, Pan SJ, Tsang IW, Yan Y (2014) Hybrid heterogeneous transfer learning through deep learning. In: AAAI","DOI":"10.1609\/aaai.v28i1.8961"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-019-01620-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10489-019-01620-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-019-01620-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,26]],"date-time":"2023-09-26T10:35:12Z","timestamp":1695724512000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10489-019-01620-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,2,7]]},"references-count":45,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2020,6]]}},"alternative-id":["1620"],"URL":"https:\/\/doi.org\/10.1007\/s10489-019-01620-3","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,2,7]]},"assertion":[{"value":"7 February 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}