{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,10]],"date-time":"2025-06-10T15:48:58Z","timestamp":1749570538390,"version":"3.37.3"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,9,23]],"date-time":"2022-09-23T00:00:00Z","timestamp":1663891200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,9,23]],"date-time":"2022-09-23T00:00:00Z","timestamp":1663891200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61703187","62006104"],"award-info":[{"award-number":["61703187","62006104"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62006103"],"award-info":[{"award-number":["62006103"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Post-graduate Research & Practice Innovation Program of Jiangsu Province","award":["KYCX 20_2386"],"award-info":[{"award-number":["KYCX 20_2386"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Intell Syst"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Transfer learning is designed to leverage knowledge in the source domain with labels to help build classification models in the target domain where labels are scarce or even unavailable. Previous studies have shown that high-level concepts extracted from original features are more suitable for cross-domain classification tasks, so many transfer learning methods transfer knowledge by modeling high-level concepts on the original feature space. However, there are two limitations to this method: First, learning high-level concepts directly on the original feature space will reduce the proportion of shared information contained in common features in the process of knowledge transfer bridge construction. Second, only learning multiple high-level concepts on the original feature space, the latent shared information contained in the domain-specific features cannot be targeted learned, so the latent shared information in the domain-specific features cannot be effectively used. To overcome these limitations, this paper proposes a novel method named Dual-Space Transfer Learning based on an Indirect Mutual Promotion Strategy (DSTL). The DSTL method is formalized as an optimization problem based on non-negative matrix tri-factorization. DSTL first extracts the common features between domains and constructs the common feature space. Then, the learning of the high-level concepts of the common feature space and the original feature space is integrated through an indirect promotion strategy, which can enhance the learning effect of common features and domain-specific features through the mutual help of the two feature spaces. The system test on benchmark data sets shows the superiority of the DSTL method.<\/jats:p>","DOI":"10.1007\/s44196-022-00132-2","type":"journal-article","created":{"date-parts":[[2022,9,23]],"date-time":"2022-09-23T15:05:51Z","timestamp":1663945551000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Dual-Space Transfer Learning Based on an Indirect Mutual Promotion Strategy"],"prefix":"10.1007","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3392-6187","authenticated-orcid":false,"given":"Teng","family":"Cui","sequence":"first","affiliation":[]},{"given":"Jianhan","family":"Pan","sequence":"additional","affiliation":[]},{"given":"Mingjing","family":"Du","sequence":"additional","affiliation":[]},{"given":"Qingyang","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,23]]},"reference":[{"issue":"1","key":"132_CR1","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1002\/sam.10099","volume":"4","author":"F Zhuang","year":"2011","unstructured":"Zhuang, F., Luo, P., Xiong, H., He, Q., Xiong, Y., Shi, Z.: Exploiting associations between word clusters and document classes for cross-domain text categorization. Stat. Anal. Data Min. ASA Data Sci. J. 4(1), 100\u2013114 (2011). https:\/\/doi.org\/10.1002\/sam.10099","journal-title":"Stat. Anal. Data Min. ASA Data Sci. J."},{"key":"132_CR2","doi-asserted-by":"publisher","unstructured":"Long, M., Wang, J., Ding, G., Cheng, W., Zhang, X., Wang, W.: Dual transfer learning. In: Proceedings of the 2012 SIAM International Conference on Data Mining, pp. 540\u2013551 (2012). https:\/\/doi.org\/10.1137\/1.9781611972825.47. SIAM","DOI":"10.1137\/1.9781611972825.47"},{"key":"132_CR3","doi-asserted-by":"publisher","unstructured":"Zhuang, F., Luo, P., Du, C., He, Q., Shi, Z.: Triplex transfer learning: exploiting both shared and distinct concepts for text classification. In: Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, pp. 425\u2013434 (2013). https:\/\/doi.org\/10.1145\/2433396.2433449","DOI":"10.1145\/2433396.2433449"},{"issue":"7","key":"132_CR4","doi-asserted-by":"publisher","first-page":"1191","DOI":"10.1109\/TCYB.2013.2281451","volume":"44","author":"F Zhuang","year":"2014","unstructured":"Zhuang, F., Luo, P., Du, C., He, Q., Shi, Z., Xiong, H.: Triplex transfer learning: exploiting both shared and distinct concepts for text classification. IEEE Trans. Cybern. 44(7), 1191\u20131203 (2014). https:\/\/doi.org\/10.1109\/TCYB.2013.2281451","journal-title":"IEEE Trans. Cybern."},{"key":"132_CR5","doi-asserted-by":"publisher","first-page":"64120","DOI":"10.1109\/ACCESS.2020.2984571","volume":"8","author":"J Pan","year":"2020","unstructured":"Pan, J., Cui, T., Le Duy, T., Li, X., Zhang, J.: Multi-group transfer learning on multiple latent spaces for text classification. IEEE Access 8, 64120\u201364130 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.2984571","journal-title":"IEEE Access"},{"key":"132_CR6","doi-asserted-by":"publisher","unstructured":"Mahdi, F.P., Yagi, N., Kobashi, S.: Automatic teeth recognition in dental x-ray images using transfer learning based faster r-cnn. In: 2020 IEEE 50th International Symposium on Multiple-Valued Logic (ISMVL), pp. 16\u201321. IEEE (2020). https:\/\/doi.org\/10.1109\/ISMVL49045.2020.00-36.","DOI":"10.1109\/ISMVL49045.2020.00-36"},{"key":"132_CR7","doi-asserted-by":"publisher","unstructured":"Kumar, S., Naman, Talib, M., Verma, P.: Covid detection from x-ray and ct scans using transfer learning - a study. In: 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), pp. 85\u201392 (2021). https:\/\/doi.org\/10.1109\/ICAIS50930.2021.9395784","DOI":"10.1109\/ICAIS50930.2021.9395784"},{"key":"132_CR8","doi-asserted-by":"publisher","unstructured":"Arshad, M.S., Rehman, U.A., Fraz, M.M.: Plant disease identification using transfer learning. In: 2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2), pp. 1\u20135 (2021). https:\/\/doi.org\/10.1109\/ICoDT252288.2021.9441512","DOI":"10.1109\/ICoDT252288.2021.9441512"},{"key":"132_CR9","doi-asserted-by":"publisher","unstructured":"Perumal, V., Theivanithy, K.: A transfer learning model for covid-19 detection with computed tomography and sonogram images. In: 2021 Sixth International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), pp. 80\u201383 (2021). https:\/\/doi.org\/10.1109\/WiSPNET51692.2021.9419419","DOI":"10.1109\/WiSPNET51692.2021.9419419"},{"key":"132_CR10","doi-asserted-by":"publisher","unstructured":"Chiba, S., Sasaoka, H.: Basic study for transfer learning for autonomous driving in car race of model car. In: 2021 6th International Conference on Business and Industrial Research (ICBIR), pp. 138\u2013141 (2021). https:\/\/doi.org\/10.1109\/ICBIR52339.2021.9465856","DOI":"10.1109\/ICBIR52339.2021.9465856"},{"key":"132_CR11","doi-asserted-by":"publisher","unstructured":"Komil\u00a0Ugli, I.K., Hussain, A., Kim, B.S., Aich, S., Kim, H.-C.: A transfer learning approach for identification of distracted driving. In: 2021 23rd International Conference on Advanced Communication Technology (ICACT), pp. 1\u20134 (2021). https:\/\/doi.org\/10.23919\/ICACT51234.2021.9370746","DOI":"10.23919\/ICACT51234.2021.9370746"},{"key":"132_CR12","doi-asserted-by":"publisher","unstructured":"Zhu, D., Song, X., Yang, J., Cong, Y., Wang, L.: A bearing fault diagnosis method based on l1 regularization transfer learning and lstm deep learning. In: 2021 IEEE International Conference on Information Communication and Software Engineering (ICICSE), pp. 308\u2013312 (2021). https:\/\/doi.org\/10.1109\/ICICSE52190.2021.9404081","DOI":"10.1109\/ICICSE52190.2021.9404081"},{"key":"132_CR13","doi-asserted-by":"publisher","unstructured":"Zhang, Q., Lu, J., Zhang, G.: Cross-domain recommendation with multiple sources. In: 2020 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20137 (2020). https:\/\/doi.org\/10.1109\/IJCNN48605.2020.9207014","DOI":"10.1109\/IJCNN48605.2020.9207014"},{"key":"132_CR14","doi-asserted-by":"publisher","unstructured":"Dauban, N., S\u00e9nac, C., Pinquier, J., Gaillard, P.: Towards a content-based prediction of personalized musical preferences using transfer learning. In: 2021 International Conference on Content-Based Multimedia Indexing (CBMI), pp. 1\u20136 (2021). https:\/\/doi.org\/10.1109\/CBMI50038.2021.9461911","DOI":"10.1109\/CBMI50038.2021.9461911"},{"key":"132_CR15","doi-asserted-by":"publisher","unstructured":"Sun, M., Xue, D., Wang, W., Hu, Q., Yu, J.: Group-based deep transfer learning with mixed gate control for cross- domain recommendation. In: 2021 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138 (2021). https:\/\/doi.org\/10.1109\/IJCNN52387.2021.9533861","DOI":"10.1109\/IJCNN52387.2021.9533861"},{"issue":"4","key":"132_CR16","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1109\/MIS.2020.2994305","volume":"36","author":"J Zhang","year":"2021","unstructured":"Zhang, J., He, M.: Crtl: context restoration transfer learning for cross-domain recommendations. IEEE Intell. Syst. 36(4), 65\u201372 (2021). https:\/\/doi.org\/10.1109\/MIS.2020.2994305","journal-title":"IEEE Intell. Syst."},{"key":"132_CR17","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.knosys.2016.01.016","volume":"97","author":"X Hu","year":"2016","unstructured":"Hu, X., Pan, J., Li, P., Li, H., He, W., Zhang, Y.: Multi-bridge transfer learning. Knowl. Based Syst. 97, 60\u201374 (2016). https:\/\/doi.org\/10.1016\/j.knosys.2016.01.016","journal-title":"Knowl. Based Syst."},{"key":"132_CR18","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1016\/j.knosys.2015.09.017","volume":"90","author":"J Pan","year":"2015","unstructured":"Pan, J., Hu, X., Zhang, Y., Li, P., Lin, Y., Li, H., He, W., Li, L.: Quadruple transfer learning: Exploiting both shared and non-shared concepts for text classification. Knowl. Based Syst. 90, 199\u2013210 (2015). https:\/\/doi.org\/10.1016\/j.knosys.2015.09.017","journal-title":"Knowl. Based Syst."},{"key":"132_CR19","doi-asserted-by":"publisher","unstructured":"Zhuang, F., Luo, P., Yin, P., He, Q., Shi, Z.: Concept learning for cross-domain text classification: A general probabilistic framework. In: Twenty-Third International Joint Conference on Artificial Intelligence, pp. 1960\u20131966 (2013). https:\/\/doi.org\/10.5555\/2540128.2540409","DOI":"10.5555\/2540128.2540409"},{"key":"132_CR20","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.neucom.2015.12.097","volume":"190","author":"J Pan","year":"2016","unstructured":"Pan, J., Hu, X., Li, P., Li, H., He, W., Zhang, Y., Lin, Y.: Domain adaptation via multi-layer transfer learning. Neurocomputing 190, 10\u201324 (2016). https:\/\/doi.org\/10.1016\/j.neucom.2015.12.097","journal-title":"Neurocomputing"},{"key":"132_CR21","doi-asserted-by":"publisher","unstructured":"Dai, W., Yang, Q., Xue, G.-R., Yu, Y.: Boosting for transfer learning. In: Proceedings of the 24th International Conference on Machine Learning, pp. 193\u2013200 (2007). https:\/\/doi.org\/10.1145\/1273496.1273521","DOI":"10.1145\/1273496.1273521"},{"key":"132_CR22","doi-asserted-by":"publisher","first-page":"601","DOI":"10.5555\/2976456.2976532","volume":"19","author":"J Huang","year":"2006","unstructured":"Huang, J., Gretton, A., Borgwardt, K., Sch\u00f6lkopf, B., Smola, A.: Correcting sample selection bias by unlabeled data. Adv. Neural Inf. Process. Syst. 19, 601\u2013608 (2006). https:\/\/doi.org\/10.5555\/2976456.2976532","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"132_CR23","doi-asserted-by":"publisher","unstructured":"Tan, B., Song, Y., Zhong, E., Yang, Q.: Transitive transfer learning. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1155\u20131164 (2015). https:\/\/doi.org\/10.1145\/2783258.2783295","DOI":"10.1145\/2783258.2783295"},{"key":"132_CR24","doi-asserted-by":"crossref","unstructured":"Tan, B., Zhang, Y., Pan, S.J., Yang, Q.: Distant domain transfer learning. In: AAAI, pp. 2604\u20132610 (2017). http:\/\/aaai.org\/ocs\/index.php\/AAAI\/AAAI17\/paper\/view\/14446","DOI":"10.1609\/aaai.v31i1.10826"},{"key":"132_CR25","doi-asserted-by":"publisher","unstructured":"Duan, L., Tsang, I.W., Xu, D., Chua, T.-S.: Domain adaptation from multiple sources via auxiliary classifiers. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 289\u2013296 (2009). https:\/\/doi.org\/10.1145\/1553374.1553411","DOI":"10.1145\/1553374.1553411"},{"key":"132_CR26","unstructured":"Long, M., Zhu, H., Wang, J., Jordan, M.I.: Deep transfer learning with joint adaptation networks. In: Proceedings of the 34th International Conference on Machine Learning, vol. 70, pp. 2208\u20132217 (2017). https:\/\/proceedings.mlr.press\/v70\/long17a.html"},{"issue":"1","key":"132_CR27","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1109\/JPROC.2020.3004555","volume":"109","author":"F Zhuang","year":"2021","unstructured":"Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., Xiong, H., He, Q.: A comprehensive survey on transfer learning. Proc. IEEE 109(1), 43\u201376 (2021). https:\/\/doi.org\/10.1109\/JPROC.2020.3004555","journal-title":"Proc. IEEE"},{"issue":"11","key":"132_CR28","doi-asserted-by":"publisher","first-page":"4709","DOI":"10.1109\/TCYB.2019.2891577","volume":"50","author":"D Wang","year":"2020","unstructured":"Wang, D., Lu, C., Wu, J., Liu, H., Zhang, W., Zhuang, F., Zhang, H.: Softly associative transfer learning for cross-domain classification. IEEE Trans. Cybern. 50(11), 4709\u20134721 (2020). https:\/\/doi.org\/10.1109\/TCYB.2019.2891577","journal-title":"IEEE Trans. Cybern."},{"key":"132_CR29","doi-asserted-by":"publisher","unstructured":"Ding, C., Li, T., Peng, W., Park, H.: Orthogonal nonnegative matrix t-factorizations for clustering. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 126\u2013135 (2006). https:\/\/doi.org\/10.1145\/1150402.1150420","DOI":"10.1145\/1150402.1150420"},{"issue":"6755","key":"132_CR30","doi-asserted-by":"publisher","first-page":"788","DOI":"10.1038\/44565","volume":"401","author":"DD Lee","year":"1999","unstructured":"Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 401(6755), 788\u2013791 (1999). https:\/\/doi.org\/10.1038\/44565","journal-title":"Nature"},{"issue":"1","key":"132_CR31","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1023\/A:1007617005950","volume":"42","author":"T Hofmann","year":"2001","unstructured":"Hofmann, T.: Unsupervised learning by probabilistic latent semantic analysis. Mach. Learn. 42(1), 177\u2013196 (2001). https:\/\/doi.org\/10.1023\/A:1007617005950","journal-title":"Mach. Learn."},{"key":"132_CR32","doi-asserted-by":"publisher","first-page":"396","DOI":"10.1016\/j.ins.2014.03.128","volume":"279","author":"OA Arqub","year":"2014","unstructured":"Arqub, O.A., Abo-Hammour, Z.: Numerical solution of systems of second-order boundary value problems using continuous genetic algorithm. Inf. Sci. 279, 396\u2013415 (2014). https:\/\/doi.org\/10.1016\/j.ins.2014.03.128","journal-title":"Inf. Sci."},{"key":"132_CR33","doi-asserted-by":"publisher","DOI":"10.1155\/2013\/831657","author":"Z Abo-Hammour","year":"2013","unstructured":"Abo-Hammour, Z., Alsmadi, O., Momani, S., Abu Arqub, O.: A genetic algorithm approach for prediction of linear dynamical systems. Math. Probl. Eng. (2013). https:\/\/doi.org\/10.1155\/2013\/831657","journal-title":"Math. Probl. Eng."},{"key":"132_CR34","doi-asserted-by":"publisher","DOI":"10.1155\/2014\/401696","author":"Z Abo-Hammour","year":"2014","unstructured":"Abo-Hammour, Z., Abu Arqub, O., Momani, S., Shawagfeh, N.: Optimization solution of troesch\u2019s and bratu\u2019s problems of ordinary type using novel continuous genetic algorithm. Discrete Dyn. Nat. Soc. (2014). https:\/\/doi.org\/10.1155\/2014\/401696","journal-title":"Discrete Dyn. Nat. Soc."},{"issue":"6","key":"132_CR35","doi-asserted-by":"publisher","first-page":"2809","DOI":"10.12785\/amis\/080617","volume":"8","author":"Z Abo-Hammour","year":"2014","unstructured":"Abo-Hammour, Z., Momani, S., Alsaedi, A.: An optimization algorithm for solving systems of singular boundary value problems. Appl. Math. Inf. Sci. 8(6), 2809 (2014). https:\/\/doi.org\/10.12785\/amis\/080617","journal-title":"Appl. Math. Inf. Sci."},{"key":"132_CR36","doi-asserted-by":"publisher","unstructured":"Hosmer\u00a0Jr, D.W., Lemeshow, S., Sturdivant, R.X.: Applied Logistic Regression, vol. 398 (2013). https:\/\/doi.org\/10.1002\/9781118548387","DOI":"10.1002\/9781118548387"},{"key":"132_CR37","doi-asserted-by":"publisher","unstructured":"Chen, Z., Zhang, W.: Domain adaptation with topical correspondence learning. In: Twenty-Third International Joint Conference on Artificial Intelligence, pp. 1280\u20131286 (2013). https:\/\/doi.org\/10.5555\/2540128.2540313","DOI":"10.5555\/2540128.2540313"},{"issue":"10","key":"132_CR38","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2009","unstructured":"Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345\u20131359 (2009). https:\/\/doi.org\/10.1109\/TKDE.2009.191","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"132_CR39","doi-asserted-by":"publisher","unstructured":"Lee, D.D., Seung, H.S.: Algorithms for non-negative matrix factorization. In: Proceedings of the 13th International Conference on Neural Information Processing Systems, pp. 535\u2013541 (2000). https:\/\/doi.org\/10.5555\/3008751.3008829","DOI":"10.5555\/3008751.3008829"}],"container-title":["International Journal of Computational Intelligence Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-022-00132-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44196-022-00132-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-022-00132-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,23]],"date-time":"2022-09-23T15:21:40Z","timestamp":1663946500000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44196-022-00132-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,23]]},"references-count":39,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["132"],"URL":"https:\/\/doi.org\/10.1007\/s44196-022-00132-2","relation":{},"ISSN":["1875-6883"],"issn-type":[{"type":"electronic","value":"1875-6883"}],"subject":[],"published":{"date-parts":[[2022,9,23]]},"assertion":[{"value":"28 March 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 August 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 September 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}],"article-number":"83"}}