{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T06:59:47Z","timestamp":1760597987026,"version":"build-2065373602"},"reference-count":37,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2020,10,16]],"date-time":"2020-10-16T00:00:00Z","timestamp":1602806400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Distribution mismatch caused by various resolutions, backgrounds, etc. can be easily found in multi-sensor systems. Domain adaptation attempts to reduce such domain discrepancy by means of different measurements, e.g., maximum mean discrepancy (MMD). Despite their success, such methods often fail to guarantee the separability of learned representation. To tackle this issue, we put forward a novel approach to jointly learn both domain-shared and discriminative representations. Specifically, we model the feature discrimination explicitly for two domains. Alternating discriminant optimization is proposed to obtain discriminative features with an l2 constraint in labeled source domain and sparse filtering is introduced to capture the intrinsic structures exists in the unlabeled target domain. Finally, they are integrated in a unified framework along with MMD to align domains. Extensive experiments compared with state-of-the-art methods verify the effectiveness of our method on cross-domain tasks.<\/jats:p>","DOI":"10.3390\/s20205868","type":"journal-article","created":{"date-parts":[[2020,10,17]],"date-time":"2020-10-17T05:45:51Z","timestamp":1602913551000},"page":"5868","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Discriminative Sparse Filtering for Multi-Source Image Classification"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5809-9988","authenticated-orcid":false,"given":"Chao","family":"Han","sequence":"first","affiliation":[{"name":"School of Electronics and Information, Northwestern Polytechnical University, Xi\u2019an 710072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Deyun","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Electronics and Information, Northwestern Polytechnical University, Xi\u2019an 710072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhen","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Electronics and Information, Northwestern Polytechnical University, Xi\u2019an 710072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1188-2120","authenticated-orcid":false,"given":"Kai","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electronics and Information, Northwestern Polytechnical University, Xi\u2019an 710072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A survey on transfer learning","volume":"22","author":"Pan","year":"2010","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.neucom.2018.05.083","article-title":"Deep visual domain adaptation: A survey","volume":"312","author":"Wang","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1109\/TPAMI.2018.2880750","article-title":"Open Set Domain Adaptation for Image and Action Recognition","volume":"42","author":"Busto","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_4","first-page":"1","article-title":"DACH: Domain Adaptation Without Domain Information","volume":"99","author":"Cai","year":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_5","unstructured":"Zhao, H., Des Combes, R.T., Zhang, K., and Gordon, G. (2019). On Learning Invariant Representation for Domain Adaptation. arXiv."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"107173","DOI":"10.1016\/j.patcog.2019.107173","article-title":"Unsupervised domain adaptive re-identification: Theory and practice","volume":"102","author":"Song","year":"2020","journal-title":"Pattern Recognit."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Dai, W., Yang, Q., Xue, G.R., and Yu, Y. (2007, January 20\u201324). Boosting for transfer learning. Proceedings of the 24th International Conference on Machine Learning, Corvalis, OR, USA.","DOI":"10.1145\/1273496.1273521"},{"key":"ref_8","unstructured":"Ben-David, S., Blitzer, J., Crammer, K., and Pereira, F. (2006, January 4\u20135). Analysis of representations for domain adaptation. Proceedings of the Neural Information Processing Systems, Vancouver, BC, Canada."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1109\/TNN.2010.2091281","article-title":"Domain adaptation via transfer component analysis","volume":"22","author":"Pan","year":"2011","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1007\/978-3-540-75225-7_5","article-title":"A Hilbert Space Embedding for Distributions","volume":"4754","author":"Smola","year":"2007","journal-title":"Int. Conf. Algorithmic Learn. Theory"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Long, M., Wang, J., Ding, G., Sun, J., and Yu, P.S. (2013, January 1\u20138). Transfer Feature Learning with Joint Distribution Adaptation. Proceedings of the International Conference on Computer Vision, Sydney, Australia.","DOI":"10.1109\/ICCV.2013.274"},{"key":"ref_12","unstructured":"Gong, B., Shi, Y., Sha, F., and Grauman, K. (2012, January 16-21). Geodesic flow kernel for unsupervised domain adaptation. Proceedings of the Computer Vision and Pattern Recognition, Providence, RI, USA."},{"key":"ref_13","unstructured":"Yosinski, J., Clune, J., Bengio, Y., and Lipson, H. (2014, January 8\u201313). How transferable are features in deep neural networks?. Proceedings of the Neural Information Processing Systems, Montreal, Canada."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ghifary, M., Kleijn, W.B., and Zhang, M. (2014). Domain adaptive neural networks for object recognition. arXiv.","DOI":"10.1007\/978-3-319-13560-1_76"},{"key":"ref_15","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). Imagenet classification with deep convolutional neural networks. Proceedings of the Neural Information Processing Systems, Lake Tahoe, NV, USA."},{"key":"ref_16","unstructured":"Tzeng, E., Hoffman, J., Zhang, N., Saenko, K., and Darrell, T. (2014). Deep domain confusion: Maximizing for domain invariance. arXiv."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3071","DOI":"10.1109\/TPAMI.2018.2868685","article-title":"Transferable Representation Learning with Deep Adaptation Networks","volume":"41","author":"Long","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_18","unstructured":"Long, M., Zhu, H., Wang, J., and Jordan, M.I. (2016, January 5\u201310). Unsupervised domain adaptation with residual transfer networks. Proceedings of the Neural Information Processing Systems, Barcelona, Spain."},{"key":"ref_19","unstructured":"Ganin, Y., and Lempitsky, V.S. (2015, January 6\u201311). Unsupervised Domain Adaptation by Backpropagation. Proceedings of the International Conference on Machine Learning, Lille, France."},{"key":"ref_20","unstructured":"Long, M., Zhu, H., Wang, J., and Jordan, M.I. (2017, January 6\u201311). Deep transfer learning with joint adaptation networks. Proceedings of the International Conference on Machine Learning, Sydney, Australia."},{"key":"ref_21","unstructured":"Pei, Z., Cao, Z., Long, M., and Wang, J. (2017, January 4\u20139). Multi-Adversarial Domain Adaptation. Proceedings of the National Conference on Artificial Intelligence, San Francisco, CA, USA."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhang, W., Ouyang, W., Li, W., and Xu, D. (2018, January 18\u201322). Collaborative and Adversarial Network for Unsupervised Domain Adaptation. Proceedings of the Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00400"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1186\/s40537-017-0103-6","article-title":"Some dimension reduction strategies for the analysis of survey data","volume":"4","author":"Weng","year":"2017","journal-title":"J. Big Data"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the dimensionality of data with neural networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"key":"ref_25","unstructured":"Le, Q.V., Karpenko, A., Ngiam, J., and Ng, A.Y. (2011, January 12\u201314). ICA with reconstruction cost for efficient overcomplete feature learning. Proceedings of the Neural Information Processing Systems, Granada, Spain."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"d\u2019Aspremont, A., Ghaoui, L.E., Jordan, M.I., and Lanckriet, G.R. (2005, January 5\u20138). A direct formulation for sparse PCA using semidefinite programming. Proceedings of the Neural Information Processing Systems, Vancouver, BC, Canada.","DOI":"10.2139\/ssrn.563524"},{"key":"ref_27","unstructured":"Ngiam, J., Chen, Z., Bhaskar, S.A., Koh, P.W., and Ng, A.Y. (2011, January 12\u201315). Sparse filtering. Proceedings of the Neural Information Processing Systems, Granada, Spain."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1109\/ACCESS.2015.2430359","article-title":"A Survey of Sparse Representation: Algorithms and Applications","volume":"3","author":"Zhang","year":"2015","journal-title":"IEEE Access"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1519","DOI":"10.1109\/TKDE.2014.2373376","article-title":"Domain Invariant Transfer Kernel Learning","volume":"27","author":"Long","year":"2015","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Sun, B., Feng, J., and Saenko, K. (2016, January 12\u201317). Return of frustratingly easy domain adaptation. Proceedings of the National Conference on Artificial Intelligence, Phoenix, AR, USA.","DOI":"10.1609\/aaai.v30i1.10306"},{"key":"ref_31","unstructured":"Goodfellow, I., Pougetabadie, J., Mirza, M., Xu, B., Wardefarley, D., Ozair, S., Courville, A., and Bengio, Y. (2014, January 8\u201313). Generative Adversarial Nets. Proceedings of the Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"131","DOI":"10.3233\/IDA-1997-1302","article-title":"Feature selection for classification","volume":"1","author":"Dash","year":"1997","journal-title":"Intell. Data Anal."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/2200000016","article-title":"Distributed optimization and statistical learning via the alternating direction method of multipliers","volume":"3","author":"Boyd","year":"2011","journal-title":"Found. Trends Mach. Learn."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Wang, Q., Bu, P., and Breckon, T.P. (2019, January 14\u201319). Unifying Unsupervised Domain Adaptation and Zero-Shot Visual Recognition. Proceedings of the International Joint Conference on Neural Network, Budapest, Hungary.","DOI":"10.1109\/IJCNN.2019.8852015"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"107254","DOI":"10.1016\/j.patcog.2020.107254","article-title":"Visual Domain Adaptation Based on Modified A Distance and Sparse Filtering","volume":"104","author":"Han","year":"2020","journal-title":"Pattern Recognit."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Wang, Q., and Breckon, T.P. (2020, January 7\u201312). Unsupervised Domain Adaptation via Structured Prediction Based Selective Pseudo-Labeling. Proceedings of the National Conference on Artificial Intelligence, New York, NY, USA.","DOI":"10.1609\/aaai.v34i04.6091"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Han, C., Lei, Y., Xie, Y., Zhou, D., and Gong, M. (2020). Learning Smooth Representations with Generalized Softmax for Unsupervised Domain Adaptation. Inf. Sci.","DOI":"10.1016\/j.ins.2020.08.075"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/20\/5868\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:22:51Z","timestamp":1760178171000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/20\/5868"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,16]]},"references-count":37,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2020,10]]}},"alternative-id":["s20205868"],"URL":"https:\/\/doi.org\/10.3390\/s20205868","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2020,10,16]]}}}