{"type":"ScholarlyArticle","title":"Learning Discriminative Correlation Subspace for Heterogeneous Domain Adaptation","authors":[{"name":"Yuguang Yan","slug":"yuguang-yan","given":"Yuguang","family":"Yan","affiliation":"School of Software Engineering , South China University of Technology , China"},{"name":"Wen Li","slug":"wen-li","given":"Wen","family":"Li","affiliation":"Computer Vision Laboratory , ETH Zurich , Switzerland"},{"name":"Michael Ng","slug":"michael-ng","given":"Michael","family":"Ng","affiliation":"Department of Mathematics , Hong Kong Baptist University , Hong Kong , China"},{"name":"Mingkui Tan","slug":"mingkui-tan","given":"Mingkui","family":"Tan","affiliation":"School of Software Engineering , South China University of Technology , China"},{"name":"Hanrui Wu","slug":"hanrui-wu","given":"Hanrui","family":"Wu","affiliation":"School of Software Engineering , South China University of Technology , China"},{"name":"Huaqing Min","slug":"huaqing-min","given":"Huaqing","family":"Min","affiliation":"School of Software Engineering , South China University of Technology , China"},{"name":"Qingyao Wu","slug":"qingyao-wu","given":"Qingyao","family":"Wu","affiliation":"School of Software Engineering , South China University of Technology , China"}],"abstract":"Domain adaptation aims to reduce the effort on collecting and annotating target data by leveraging knowledge from a different source domain. The domain adaptation problem will become extremely challenging when the feature spaces of the source and target domains are different, which is also known as the heterogeneous domain adaptation (HDA) problem. In this paper, we propose a novel HDA method to find the optimal discriminative correlation subspace for the source and target data. The discriminative correlation subspace is inherited from the canonical correlation subspace between the source and target data,  and is further optimized to maximize the discriminative ability for the target domain classifier. We formulate a joint objective in order to simultaneously learn the discriminative correlation subspace and the target domain classifier. We then apply an alternating direction method of multiplier (ADMM) algorithm to address the resulting non-convex optimization problem. Comprehensive experiments on two real-world data sets demonstrate the effectiveness of the proposed method compared to the state-of-the-art methods.","doi":"10.24963/ijcai.2017/454","url":"https://ijcai.org/proceedings/2017/454","pdf":"https://ijcai.org/proceedings/2017/0454.pdf","markdown":"https://ijcai.org/proceedings/2017/454.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/454","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Machine Learning S-Z","track":"Main track","number":454,"pages":{"first":3252,"last":3258},"keywords":["Machine Learning: Transfer, Adaptation, Multi-task Learning","Machine Learning: Classification"],"videos":[],"references":[{"position":1,"raw":"[Bay et al., 2006] Herbert Bay, Tinne Tuytelaars, and Luc Van Gool. Surf: Speeded up robust features. In ECCV, pages 404-417, 2006.","title":"Surf: Speeded up robust features","authors":"Bay","year":"2006","venue":"ECCV","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Boyd et al., 2011] Stephen Boyd, Neal Parikh, Eric Chu, Borja Peleato, and Jonathan Eckstein. Distributed opti- mization and statistical learning via the alternating direc- tion method of multipliers. Foundations and Trends R in Machine Learning, 3(1):1-122, 2011.","title":"Distributed optimization and statistical learning via the alternating direction method of multipliers","authors":"Stephen Boyd, Neal Parikh, Eric Chu, Borja Peleato, Jonathan Eckstein","year":"2011","venue":"Foundations and Trends R in Machine Learning","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Chang and Lin, 2011] Chih-Chung Chang and Chih-Jen Lin. LIBSVM: a library for support vector machines. TIST, 2(3):27, 2011.","title":"LIBSVM: a library for support vector machines","authors":"Chang, Lin ; ] Chih-Chung Chang, Chih-Jen Lin","year":"2011","venue":"TIST","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Chen and Zhang, 2013] Zheng Chen and Weixiong Zhang. Domain adaptation with topical correspondence learning. In IJCAI, 2013.","title":"Domain adaptation with topical correspondence learning","authors":"Zheng Chen, Weixiong Zhang","year":"2013","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2013/193"},{"position":5,"raw":"[Chu et al., 2013] Delin Chu, Li-Zhi Liao, Michael K Ng, and Xiaowei Zhang. Sparse canonical correlation analysis: New formulation and algorithm. T-PAMI, 35(12):3050- 3065, 2013.","title":"Sparse canonical correlation analysis: New formulation and algorithm","authors":"Delin Chu, Li-Zhi Liao, Michael K Ng, Xiaowei Zhang","year":"2013","venue":"T-PAMI","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Donahue et al., 2014] Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, and Trevor Darrell. Decaf: A deep convolutional activation feature for generic visual recognition. In ICML, vol- ume 32, pages 647-655, 2014.","title":"Decaf: A deep convolutional activation feature for generic visual recognition","authors":"Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, Trevor Darrell","year":"2014","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Duan et al., 2010] L Duan, D Xu, I Tsang, and J Luo. Visual event recognition in videos by learning from web data. In CVPR, 2010.","title":"Visual event recognition in videos by learning from web data","authors":"Duan, I Xu, Tsang, Luo","year":"2010","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Duan et al., 2012a] Lixin Duan, Dong Xu, and Ivor W Tsang. Learning with augmented features for heteroge- neous domain adaptation. In ICML, pages 711-718, 2012.","title":"Learning with augmented features for heterogeneous domain adaptation","authors":"Duan","year":"2012","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Duan et al., 2012b] Lixin Duan, Dong Xu, Ivor Wai-Hung Tsang, and Jiebo Luo. Visual event recognition in videos by learning from web data. T-PAMI, 34(9):1667-1680, 2012.","title":"Visual event recognition in videos by learning from web data","authors":"Duan","year":"2012","venue":"T-PAMI","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Griffin et al., 2007] Gregory Griffin, Alex Holub, and Pietro Perona. Caltech-256 object category dataset. 2007.","title":"Caltech-256 object category dataset","authors":"Gregory Griffin, Alex Holub, Pietro Perona","year":"2007","venue":null,"doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Hoffman et al., 2014] Judy Hoffman, Erik Rodner, Jeff Donahue, Brian Kulis, and Kate Saenko. Asymmetric and category invariant feature transformations for domain adaptation. IJCV, 109(1-2):28-41, 2014.","title":"Asymmetric and category invariant feature transformations for domain adaptation","authors":"Judy Hoffman, Erik Rodner, Jeff Donahue, Brian Kulis, Kate Saenko","year":"2014","venue":"IJCV","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Hubert Tsai et al., 2016] Yao-Hung Hubert Tsai, Yi-Ren Yeh, and Yu-Chiang Frank Wang. Learning cross-domain landmarks for heterogeneous domain adaptation. In CVPR, pages 5081-5090, 2016.","title":"Learning cross-domain landmarks for heterogeneous domain adaptation","authors":"Yao-Hung Hubert Tsai, Yi-Ren Yeh, Yu-Chiang Frank, Wang","year":"2016","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Kulis et al., 2011] Brian Kulis, Kate Saenko, and Trevor Darrell. What you saw is not what you get: Domain adaptation using asymmetric kernel transforms. In CVPR, pages 1785-1792, 2011.","title":"What you saw is not what you get: Domain adaptation using asymmetric kernel transforms","authors":"Brian Kulis, Kate Saenko, Trevor Darrell","year":"2011","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Li et al., 2014] Wen Li, Lixin Duan, Dong Xu, and Ivor W Tsang. Learning with augmented features for supervised and semi-supervised heterogeneous domain adaptation. T- PAMI, 36(6):1134-1148, 2014.","title":"Learning with augmented features for supervised and semi-supervised heterogeneous domain adaptation","authors":"Wen Li, Lixin Duan, Dong Xu, Ivor W Tsang","year":"2014","venue":"T-PAMI","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Pan and Yang, 2010] Sinno Jialin Pan and Qiang Yang. A survey on transfer learning. TKDE, 22(10):1345-1359, 2010.","title":"A survey on transfer learning","authors":"Jialin Sinno, Qiang Pan, Yang","year":"2010","venue":"TKDE","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Patel et al., 2015] Vishal M Patel, Raghuraman Gopalan, Ruonan Li, and Rama Chellappa. Visual domain adapta- tion: A survey of recent advances. IEEE Signal Processing Magazine, 32(3):53-69, 2015.","title":"Visual domain adaptation: A survey of recent advances","authors":"M Vishal, Raghuraman Patel, Ruonan Gopalan, Rama Li, Chellappa","year":"2015","venue":"IEEE Signal Processing Magazine","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Saenko et al., 2010] Kate Saenko, Brian Kulis, Mario Fritz, and Trevor Darrell. Adapting visual category models to new domains. In ECCV, pages 213-226, 2010.","title":"Adapting visual category models to new domains","authors":"Saenko","year":"2010","venue":"ECCV","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Schmidt, 2005] Mark Schmidt. minfunc: unconstrained differentiable multivariate optimization in matlab. http://www.cs.ubc.ca/ schmidtm/Software/minFunc.html, 2005.","title":"minfunc: unconstrained differentiable multivariate optimization in matlab","authors":"Mark Schmidt","year":null,"venue":null,"doi":null,"url":"http://www.cs.ubc.ca/schmidtm/Software/minFunc.html,2005","internal_url":null},{"position":19,"raw":"[Shi et al., 2010] Xiaoxiao Shi, Qi Liu, Wei Fan, S Yu Philip, and Ruixin Zhu. Transfer learning on heterogenous feature spaces via spectral transformation. In ICDM, pages 1049- 1054, 2010.","title":"Transfer learning on heterogenous feature spaces via spectral transformation","authors":"Xiaoxiao Shi, Qi Liu, Wei Fan, Yu Philip, Ruixin Zhu","year":"2010","venue":"ICDM","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Tan et al., 2015] Ben Tan, Yangqiu Song, Erheng Zhong, and Qiang Yang. Transitive transfer learning. In KDD, pages 1155-1164, 2015.","title":"Transitive transfer learning","authors":"Ben Tan, Yangqiu Song, Erheng Zhong, Qiang Yang","year":"2015","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Wang and Mahadevan, 2011] Chang Wang and Sridhar Ma- hadevan. Heterogeneous domain adaptation using mani- fold alignment. In IJCAI, volume 22, page 1541, 2011.","title":"Heterogeneous domain adaptation using manifold alignment","authors":"Chang Wang, Sridhar Mahadevan","year":"2011","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2011/259"},{"position":22,"raw":"[Wu et al., 2013] Xinxiao Wu, Han Wang, Cuiwei Liu, and Yunde Jia. Cross-view action recognition over heteroge- neous feature spaces. In ICCV, pages 609-616, 2013.","title":"Cross-view action recognition over heterogeneous feature spaces","authors":"Wu","year":"2013","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Wu et al., 2014] Qingyao Wu, Michael K. Ng, and Yun- ming Ye. Cotransfer learning using coupled markov chains with restart. IEEE Intelligent Systems, 29(4):26 -33, 2014.","title":"Cotransfer learning using coupled markov chains with restart","authors":"Qingyao Wu, Michael K Ng, Yunming Ye","year":"2014","venue":"IEEE Intelligent Systems","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Xiao and Guo, 2015] Min Xiao and Yuhong Guo. Feature space independent semi-supervised domain adaptation via kernel matching. T-PAMI, 37(1):54-66, 2015.","title":"Feature space independent semi-supervised domain adaptation via kernel matching","authors":"Xiao, Guo ; ] Min, Xiao, Yuhong Guo","year":"2015","venue":"T-PAMI","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Yang et al., 2015] Liu Yang, Liping Jing, Jian Yu, and Michael K Ng. Learning transferred weights from co- occurrence data for heterogeneous transfer learning. T- NNLS, 2015.","title":"Learning transferred weights from cooccurrence data for heterogeneous transfer learning","authors":"Liu Yang, Liping Jing, Jian Yu, Michael K Ng","year":"2015","venue":null,"doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Zhou et al., 2014a] Joey Tianyi Zhou, Sinno Jialin Pan, Ivor W Tsang, and Yan Yan. Hybrid heterogeneous trans- fer learning through deep learning. In AAAI, pages 2213- 2220, 2014.","title":"Hybrid heterogeneous transfer learning through deep learning","authors":"Zhou","year":"2014","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Zhou et al., 2014b] Joey Tianyi Zhou, Ivor W Tsang, Sinno Jialin Pan, and Mingkui Tan. Heterogeneous do- main adaptation for multiple classes. In AISTATS, pages 1095-1103, 2014.","title":"Heterogeneous domain adaptation for multiple classes","authors":"Zhou","year":"2014","venue":"AISTATS","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Zhou et al., 2016] Joey Tianyi Zhou, Sinno Jialin Pan, Ivor W Tsang, and Shen-Shyang Ho. Transfer learning for cross-language text categorization through active cor- respondences construction. In AAAI, pages 2400-2406, 2016.","title":"Transfer learning for cross-language text categorization through active correspondences construction","authors":"Joey Tianyi Zhou, Sinno Jialin Pan, Ivor W Tsang, Shen-Shyang Ho","year":"2016","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Zhu et al., 2011] Yin Zhu, Yuqiang Chen, Zhongqi Lu, Sinno Jialin Pan, Gui-Rong Xue, Yong Yu, and Qiang Yang. Heterogeneous transfer learning for image classi- fication. In AAAI, 2011.","title":"Heterogeneous transfer learning for image classification","authors":"Yin Zhu, Yuqiang Chen, Zhongqi Lu, Sinno Jialin Pan, Gui-Rong Xue, Yong Yu, Qiang Yang","year":"2011","venue":"AAAI","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}