Cross-Domain Recognition by Identifying Joint Subspaces of Source Domain and Target Domain

Lin, Yuewei; Chen, Jing; Cao, Yu; Zhou, Youjie; Zhang, Lingfeng; Tang, Yuan Yan; Wang, Song

doi:10.1109/TCYB.2016.2538199

Cross-Domain Recognition by Identifying Joint Subspaces of Source Domain and Target Domain

Journal Article · Mon Mar 21 00:00:00 EDT 2016 · IEEE Transactions on Cybernetics

DOI:https://doi.org/10.1109/TCYB.2016.2538199· OSTI ID:1491680

Lin, Yuewei ^[1]; Chen, Jing ^[2]; Cao, Yu ^[3]; Zhou, Youjie ^[1]; Zhang, Lingfeng ^[4]; Tang, Yuan Yan ^[5]; Wang, Song ^[1]

Univ. of South Carolina, Columbia, SC (United States). Dept. of Computer Science and Engineering
Chongqing Univ. (China)
IBM Almaden Research Center, San Jose, CA (United States); Univ. of South Carolina, Columbia, SC (United States)
Univ. of Houston, TX (United States)
Univ. of Macau (China)

This paper introduces here a new method to solve the cross-domain recognition problem. Different from the traditional domain adaption methods which rely on a global domain shift for all classes between the source and target domains, the proposed method is more flexible to capture individual class variations across domains. By adopting a natural and widely used assumption that the data samples from the same class should lay on an intrinsic low-dimensional subspace, even if they come from different domains, the proposed method circumvents the limitation of the global domain shift, and solves the cross-domain recognition by finding the joint subspaces of the source and target domains. Specifically, given labeled samples in the source domain, we construct a subspace for each of the classes. Then we construct subspaces in the target domain, called anchor subspaces, by collecting unlabeled samples that are close to each other and are highly likely to belong to the same class. The corresponding class label is then assigned by minimizing a cost function which reflects the overlap and topological structure consistency between subspaces across the source and target domains, and within the anchor subspaces, respectively. We further combine the anchor subspaces to the corresponding source subspaces to construct the joint subspaces. Subsequently, one-versus-rest support vector machine classifiers are trained using the data samples belonging to the same joint subspaces and applied to unlabeled data in the target domain. We evaluate the proposed method on two widely used datasets: 1) object recognition dataset for computer vision tasks and 2) sentiment classification dataset for natural language processing tasks. Comparison results demonstrate that the proposed method outperforms the comparison methods on both datasets.

View Accepted Manuscript (DOE)

Research Organization:: Chongqing Univ. (China); Univ. of Macau (China); Univ. of South Carolina, Columbia, SC (United States)

Sponsoring Organization:: Air Force Office of Scientific Research (AFOSR); Army Research Laboratory (ARL); National Natural Science Foundation of China (NSFC); National Science Foundation (NSF); Research Grants of University of Macau (China); Science and Technology Development Fund of Macau (China); USDOE Office of Science (SC)

Grant/Contract Number:: SC0012704

OSTI ID:: 1491680

Report Number(s):: BNL--210903-2019-JAAM

Journal Information:: IEEE Transactions on Cybernetics, Journal Name: IEEE Transactions on Cybernetics Journal Issue: 4 Vol. 47; ISSN 2168-2267

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

References (38)

Speeded-Up Robust Features (SURF) Bay, Herbert; Ess, Andreas; Tuytelaars, Tinne Computer Vision and Image Understanding, Vol. 110, Issue 3 https://doi.org/10.1016/j.cviu.2007.09.014	journal	June 2008
Multiview Hessian discriminative sparse coding for image annotation Liu, Weifeng; Tao, Dacheng; Cheng, Jun Computer Vision and Image Understanding, Vol. 118 https://doi.org/10.1016/j.cviu.2013.03.007	journal	January 2014
How Does the Brain Solve Visual Object Recognition? DiCarlo, James J.; Zoccolan, Davide; Rust, Nicole C. Neuron, Vol. 73, Issue 3 https://doi.org/10.1016/j.neuron.2012.01.010	journal	February 2012
Unbiased look at dataset bias Torralba, Antonio; Efros, Alexei A. CVPR 2011 https://doi.org/10.1109/CVPR.2011.5995347	conference	June 2011
Discriminative Subspace Clustering Zografos, Vasileios; Ellis, Liam; Mester, Rudolf 2013 IEEE Conference on Computer Vision and Pattern Recognition https://doi.org/10.1109/CVPR.2013.274	conference	June 2013
Generalized Domain-Adaptive Dictionaries Shekhar, Sumit; Patel, Vishal M.; Nguyen, Hien V. 2013 IEEE Conference on Computer Vision and Pattern Recognition https://doi.org/10.1109/CVPR.2013.53	conference	June 2013
Semi-supervised Domain Adaptation with Instance Constraints Donahue, Jeff; Hoffman, Judy; Rodner, Erik 2013 IEEE Conference on Computer Vision and Pattern Recognition https://doi.org/10.1109/CVPR.2013.92	conference	June 2013
Subspace Interpolation via Dictionary Learning for Unsupervised Domain Adaptation Ni, Jie; Qiu, Qiang; Chellappa, Rama 2013 IEEE Conference on Computer Vision and Pattern Recognition https://doi.org/10.1109/CVPR.2013.95	conference	June 2013
Transfer Joint Matching for Unsupervised Domain Adaptation Long, Mingsheng; Wang, Jianmin; Ding, Guiguang 2014 IEEE Conference on Computer Vision and Pattern Recognition https://doi.org/10.1109/CVPR.2014.183	conference	June 2014
Domain Adaptation on the Statistical Manifold Baktashmotlagh, Mahsa; Harandi, Mehrtash T.; Lovell, Brian C. 2014 IEEE Conference on Computer Vision and Pattern Recognition https://doi.org/10.1109/CVPR.2014.318	conference	June 2014
Beyond the shortest path: Unsupervised domain adaptation by Sampling Subspaces along the Spline Flow Caseiro, Rui; Henriques, Joao F.; Martins, Pedro 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) https://doi.org/10.1109/CVPR.2015.7299009	conference	June 2015
Fisher Discrimination Dictionary Learning for sparse representation Yang, Meng; Zhang, Lei; Feng, Xiangchu 2011 International Conference on Computer Vision https://doi.org/10.1109/ICCV.2011.6126286	conference	November 2011
Domain adaptation for object recognition: An unsupervised approach Gopalan, Raghuraman; Chellappa, Rama 2011 International Conference on Computer Vision https://doi.org/10.1109/ICCV.2011.6126344	conference	November 2011
Frustratingly Easy NBNN Domain Adaptation Tommasi, Tatiana; Caputo, Barbara 2013 IEEE International Conference on Computer Vision https://doi.org/10.1109/ICCV.2013.116	conference	December 2013
Unsupervised Visual Domain Adaptation Using Subspace Alignment Fernando, Basura; Habrard, Amaury; Sebban, Marc 2013 IEEE International Conference on Computer Vision https://doi.org/10.1109/ICCV.2013.368	conference	December 2013
Cross-domain recognition by identifying compact joint subspaces Lin, Yuewei; Chen, Jing; Cao, Yu 2015 IEEE International Conference on Image Processing (ICIP) https://doi.org/10.1109/ICIP.2015.7351447	conference	September 2015
Visual Domain Adaptation: A survey of recent advances Patel, Vishal M.; Gopalan, Raghuraman; Li, Ruonan IEEE Signal Processing Magazine, Vol. 32, Issue 3 https://doi.org/10.1109/MSP.2014.2347059	journal	May 2015
Flowing on Riemannian Manifold: Domain Adaptation by Shifting Covariance No authors listed IEEE Transactions on Cybernetics, Vol. 44, Issue 12 https://doi.org/10.1109/TCYB.2014.2305701	journal	December 2014
Learning to Rank Using User Clicks and Visual Features for Image Retrieval Yu, Jun; Tao, Dacheng; Wang, Meng IEEE Transactions on Cybernetics, Vol. 45, Issue 4 https://doi.org/10.1109/TCYB.2014.2336697	journal	April 2015
Rating Knowledge Sharing in Cross-Domain Collaborative Filtering No authors listed IEEE Transactions on Cybernetics, Vol. 45, Issue 5 https://doi.org/10.1109/TCYB.2014.2343982	journal	May 2015
Manifold Regularized Multitask Learning for Semi-Supervised Multilabel Image Classification Luo, Yong; Tao, Dacheng; Geng, Bo IEEE Transactions on Image Processing, Vol. 22, Issue 2 https://doi.org/10.1109/TIP.2012.2218825	journal	February 2013
Click Prediction for Web Image Reranking Using Multimodal Sparse Coding No authors listed IEEE Transactions on Image Processing, Vol. 23, Issue 5 https://doi.org/10.1109/TIP.2014.2311377	journal	May 2014
Decomposition-Based Transfer Distance Metric Learning for Image Classification Luo, Yong; Liu, Tongliang; Tao, Dacheng IEEE Transactions on Image Processing, Vol. 23, Issue 9 https://doi.org/10.1109/TIP.2014.2332398	journal	September 2014
A Survey on Transfer Learning Pan, Sinno Jialin; Yang, Qiang IEEE Transactions on Knowledge and Data Engineering, Vol. 22, Issue 10 https://doi.org/10.1109/TKDE.2009.191	journal	October 2010
Domain Adaptation via Transfer Component Analysis Pan, Sinno Jialin; Tsang, Ivor W.; Kwok, James T. IEEE Transactions on Neural Networks, Vol. 22, Issue 2 https://doi.org/10.1109/TNN.2010.2091281	journal	February 2011
Transductive Face Sketch-Photo Synthesis No authors listed IEEE Transactions on Neural Networks and Learning Systems, Vol. 24, Issue 9 https://doi.org/10.1109/TNNLS.2013.2258174	journal	September 2013
Lambertian reflectance and linear subspaces Basri, R.; Jacobs, D. W. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 25, Issue 2 https://doi.org/10.1109/TPAMI.2003.1177153	journal	February 2003
Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval No authors listed IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 28, Issue 7 https://doi.org/10.1109/TPAMI.2006.134	journal	July 2006
General Tensor Discriminant Analysis and Gabor Features for Gait Recognition Tao, Dacheng; Li, Xuelong; Wu, Xindong IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 29, Issue 10 https://doi.org/10.1109/TPAMI.2007.1096	journal	October 2007
Domain Transfer Multiple Kernel Learning No authors listed IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 34, Issue 3 https://doi.org/10.1109/TPAMI.2011.114	journal	March 2012
Unsupervised Adaptation Across Domain Shifts by Generating Intermediate Data Representations Gopalan, Raghuraman; Chellappa, Rama IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 36, Issue 11 https://doi.org/10.1109/TPAMI.2013.249	journal	November 2014
Sparse Subspace Clustering: Algorithm, Theory, and Applications Elhamifar, E.; Vidal, R. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 35, Issue 11 https://doi.org/10.1109/TPAMI.2013.57	journal	November 2013
Feature Space Independent Semi-Supervised Domain Adaptation via Kernel Matching Xiao, Min; Guo, Yuhong IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 37, Issue 1 https://doi.org/10.1109/TPAMI.2014.2343216	journal	January 2015
$rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation Aharon, M.; Elad, M.; Bruckstein, A. IEEE Transactions on Signal Processing, Vol. 54, Issue 11 https://doi.org/10.1109/TSP.2006.881199	journal	November 2006
Grassmann discriminant analysis: a unifying view on subspace-based learning Hamm, Jihun; Lee, Daniel D. Proceedings of the 25th international conference on Machine learning - ICML '08 https://doi.org/10.1145/1390156.1390204	conference	January 2008
Laplacian Eigenmaps for Dimensionality Reduction and Data Representation Belkin, Mikhail; Niyogi, Partha Neural Computation, Vol. 15, Issue 6 https://doi.org/10.1162/089976603321780317	journal	June 2003
Metrics and models for handwritten character recognition Hastie, Trevor; Simard, Patrice Y. Statistical Science, Vol. 13, Issue 1 https://doi.org/10.1214/ss/1028905973	journal	February 1998
Domain adaptation with structural correspondence learning Blitzer, John; McDonald, Ryan; Pereira, Fernando Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing - EMNLP '06 https://doi.org/10.3115/1610075.1610094	conference	January 2006

Similar Records

Related Subjects

97 MATHEMATICS AND COMPUTING
cross domain recognition
joint subspace
unsupervised

Cross-Domain Recognition by Identifying Joint Subspaces of Source Domain and Target Domain

Citation Formats

References (38)

Similar Records

Related Subjects