Cross-Domain Object Representation via Robust Low-Rank Correlation Analysis
Article No.: 126, Pages 1 - 20
Abstract
Cross-domain data has become very popular recently since various viewpoints and different sensors tend to facilitate better data representation. In this article, we propose a novel cross-domain object representation algorithm (RLRCA) which not only explores the complexity of multiple relationships of variables by canonical correlation analysis (CCA) but also uses a low rank model to decrease the effect of noisy data. To the best of our knowledge, this is the first try to smoothly integrate CCA and a low-rank model to uncover correlated components across different domains and to suppress the effect of noisy or corrupted data. In order to improve the flexibility of the algorithm to address various cross-domain object representation problems, two instantiation methods of RLRCA are proposed from feature and sample space, respectively. In this way, a better cross-domain object representation can be achieved through effectively learning the intrinsic CCA features and taking full advantage of cross-domain object alignment information while pursuing low rank representations. Extensive experimental results on CMU PIE, Office-Caltech, Pascal VOC 2007, and NUS-WIDE-Object datasets, demonstrate that our designed models have superior performance over several state-of-the-art cross-domain low rank methods in image clustering and classification tasks with various corruption levels.
References
[1]
Shotaro Akaho. 2006. A kernel method for canonical correlation analysis. arXiv: Learning (2006).
[2]
Samat Alim, Persello Claudio, Gamba Paolo, Sicong Liu, Abuduwaili Jilili, and Erzhu Li. 2017. Supervised and semi-supervised multi-view canonical correlation analysis ensemble for heterogeneous domain adaptation in remote sensing image classification. Remote Sensing 9, 4 (2017), 337. https://doi.org/10.3390/rs9040337
[3]
Galen Andrew, Raman Arora, Jeff Bilmes, and Karen Livescu. 2013. Deep canonical correlation analysis. In International Conference on Machine Learning. PMLR, 1247–1255.
[4]
Timothy Apasiba Abeo, Xiang-Jun Shen, Bing-Kun Bao, Zheng-Jun Zha, and Jianping Fan. 2019. A generalized multi-dictionary least squares framework regularized with multi-graph embeddings. Pattern Recognition 90 (2019), 1–11.
[5]
Cong Bai, Jian Chen, Qing Ma, Pengyi Hao, and Shengyong Chen. 2020. Cross-domain representation learning by domain-migration generative adversarial network for sketch based image retrieval. Journal of Visual Communication and Image Representation 71 (2020), 102835.
[6]
Avrim Blum and Tom Mitchell. 1998. Combining labeled and unlabeled data with co-training. In Proceedings of the Eleventh Annual Conference on Computational Learning Theory. 92–100.
[7]
Maria Brbic and Ivica Kopriva. 2018. Multi-view low-rank sparse subspace clustering. Pattern Recognition 73 (2018), 247–258.
[8]
Deng Cai, Xiaofei He, and Jiawei Han. 2007. Spectral regression for efficient regularized subspace learning. In Proceedings of ICCV, 1–8. https://doi.org/10.1109/ICCV.2007.4408855
[9]
Emmanuel J. Candes, Xiaodong Li, Yi Ma, and John Wright. 2011. Robust principal component analysis. J. ACM 58, 3 (2011), 11.
[10]
Venkat Chandrasekaran, Benjamin Recht, Pablo A. Parrilo, and Alan S. Willsky. 2012. The convex geometry of linear inverse problems. Foundations of Computational Mathematics 12, 6 (2012), 805–849.
[11]
Tat-Seng Chua, Jinhui Tang, Richang Hong, Haojie Li, Zhiping Luo, and Yantao Zheng. 2009. Nus-wide: a real-world web image database from national university of singapore. In Proceedings of the ACM International Conference on Image and Video Retrieval. 1–9.
[12]
Zhengming Ding and Yun Fu. 2016. Robust multi-view subspace learning through dual low-rank decompositions. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 30.
[13]
Matthias Dorfer, Jan Schlüter, Andreu Vall, Filip Korzeniowski, and Gerhard Widmer. 2018. End-to-end cross-modality retrieval with CCA projections and pairwise ranking loss. International Journal of Multimedia Information Retrieval 7, 2 (2018), 117–128.
[14]
Mark Everingham, Luc Van Gool, Christopher K. I. Williams, John Winn, and Andrew Zisserman. 2010. The Pascal visual object classes (VOC) challenge. International Journal of Computer Vision 88, 2 (2010), 303–338.
[15]
Jialu Liu, Chi Wang, Jing Gao, and Jiawei Han. 2013. Multi-view clustering via joint nonnegative matrix factorization. In Proceedings of the 2013 SIAM International Conference on Data Mining. SIAM, 252–260.
[16]
Muhammad Ghifary, W. Bastiaan Kleijn, Mengjie Zhang, and David Balduzzi. 2015. Domain generalization for object recognition with multi-task autoencoders. In Proceedings of the IEEE International Conference on Computer Vision. 2551–2559.
[17]
Gregory Griffin, Alex Holub, and Pietro Perona. 2007. Caltech-256 object category dataset. (2007).
[18]
David R. Hardoon and John Shawe-Taylor. 2003. KCCA for different level precision in content-based image retrieval. In Proceedings of 3rd International Workshop on Content-Based Multimedia Indexing (IRISA) (Rennes, France). Citeseer, 22–24.
[19]
David R. Hardoon and John Shawe-Taylor. 2011. Sparse canonical correlation analysis. Machine Learning 83, 3 (2011), 331–353.
[20]
David R. Hardoon, Sandor Szedmak, and John Shawe-Taylor. 2004. Canonical correlation analysis: An overview with application to learning methods. Neural Computation 16, 12 (2004), 2639–2664.
[21]
Harold Hotelling. 1936. Relations between two sets of variates. Biometrika 28 (1936), 321–377.
[22]
Da Li, Yongxin Yang, Yi-Zhe Song, and Timothy M. Hospedales. 2017. Deeper, broader and artier domain generalization. In Proceedings of the IEEE International Conference on Computer Vision. 5542–5550.
[23]
W. Li, Z. Xu, D. Xu, D. Dai, and L. Van Gool. 2018. Domain generalization and adaptation using low rank exemplar SVMs. IEEE Transactions on Pattern Analysis and Machine Intelligence 40, 5 (2018), 1114–1127. https://doi.org/10.1109/TPAMI.2017.2704624
[24]
Guangcan Liu, Zhouchen Lin, Shuicheng Yan, Ju Sun, Yong Yu, and Yi Ma. 2013. Robust recovery of subspace structures by low-rank representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 35, 1 (2013), 171–184.
[25]
Guangcan Liu, Zhouchen Lin, Yong Yu, et al. 2010. Robust subspace segmentation by low-rank representation. In Icml, Vol. 1.Citeseer, 8.
[26]
Junmin Liu, Yijun Chen, Jiangshe Zhang, and Zongben Xu. 2014. Enhancing low-rank subspace clustering by manifold regularization. IEEE Transactions on Image Processing 23, 9 (2014), 4022–4030.
[27]
Jiawei Liu, Zheng-Jun Zha, Di Chen, Richang Hong, and Meng Wang. 2019. Adaptive transfer network for cross-domain person re-identification. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 7202–7211.
[28]
Canyi Lu, Zhouchen Lin, and Shuicheng Yan. 2015. Smoothed low rank and sparse matrix recovery by iteratively reweighted least squares minimization. IEEE Transactions on Image Processing 24, 2 (2015), 646–654.
[29]
Allan Aasbjerg Nielsen. 2007. The regularized iteratively reweighted MAD method for change detection in multi- and hyperspectral data. IEEE Transactions on Image Processing 16, 2 (2007), 463–478.
[30]
Kate Saenko, Brian Kulis, Mario Fritz, and Trevor Darrell. 2010. Adapting visual category models to new domains. In European Conference on Computer Vision. Springer, 213–226.
[31]
Xiaobo Shen, Quansen Sun, and Yunhao Yuan. 2015. A unified multiset canonical correlation analysis framework based on graph embedding for multiple feature extraction. Neurocomputing 148 (2015), 397–408.
[32]
Xiang-Jun Shen, Si-Xing Liu, Bing-Kun Bao, Chun-Hong Pan, Zheng-Jun Zha, and Jianping Fan. 2020. A generalized least-squares approach regularized with graph embedding for dimensionality reduction. Pattern Recognition 98 (2020), 107023.
[33]
Terence Sim, Simon Baker, and Maan Bsat. 2002. The CMU pose, illumination, and expression (PIE) database. In Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition. IEEE, 53–58.
[34]
Liang Sun, Shuiwang Ji, and Jieping Ye. 2008. A least squares formulation for canonical correlation analysis. In Proceedings of the 25th International Conference on Machine Learning. 1024–1031.
[35]
Shiliang Sun and Feng Jin. 2011. Robust co-training. International Journal of Pattern Recognition and Artificial Intelligence 25, 07 (2011), 1113–1126.
[36]
Fernando De la Torre and Michael J. Black. 2003. A framework for robust subspace learning. International Journal of Computer Vision 54, 1-3 (2003), 117–142.
[37]
Rene Vidal and Paolo Favaro. 2014. Low rank subspace clustering (LRSC). Pattern Recognition Letters 43 (2014), 47–61.
[38]
Wei Wang and Zhi-Hua Zhou. 2007. Analyzing co-training style algorithms. In European Conference on Machine Learning. Springer, 454–465.
[39]
Chiapo Wei, Chihfan Chen, and Yuchiang Frank Wang. 2014. Robust face recognition with structurally incoherent low-rank matrix decomposition. IEEE Transactions on Image Processing 23, 8 (2014), 3294–3307.
[40]
Wang Wei and Zhi Hua Zhou. 2010. A new analysis of co-training. In Proceedings of the 27th International Conference on Machine Learning (ICML-10), (June 21-24, 2010, Haifa, Israel).
[41]
Daniela Witten, Robert Tibshirani, and Trevor Hastie. 2009. A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis. Biostatistics 10, 3 (2009), 515–534.
[42]
Rongkai Xia, Yan Pan, Lei Du, and Jian Yin. 2014. Robust multi-view spectral clustering via low-rank and sparse decomposition. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 28.
[43]
Chang Xu, Dacheng Tao, and Chao Xu. 2013. A survey on multi-view learning. arXiv: Learning (2013).
[44]
Chang Xu, Dacheng Tao, and Chao Xu. 2015. Multi-view learning with incomplete views. IEEE Transactions on Image Processing 24, 12 (2015), 5812–5825.
[45]
Junfeng Yang, Wotao Yin, Yin Zhang, and Yilun Wang. 2009. A fast algorithm for edge-preserving variational multichannel image restoration. Siam Journal on Imaging Sciences 2, 2 (2009), 569–592.
[46]
Zheng-Jun Zha, Chong Wang, Dong Liu, Hongtao Xie, and Yongdong Zhang. 2020. Robust deep co-saliency detection with group semantic and pyramid attention. IEEE Transactions on Neural Networks and Learning Systems 31, 7 (2020), 2398–2408.
[47]
Bo Zhang and Zhong-Zhi Shi. 2013. Classification of big velocity data via cross-domain canonical correlation analysis. In 2013 IEEE International Conference on Big Data. IEEE, 493–498.
[48]
Hanwang Zhang, Zheng-Jun Zha, Yang Yang, Shuicheng Yan, and Tat-Seng Chua. 2014. Robust (semi) nonnegative graph embedding. IEEE Transactions on Image Processing 23, 7 (2014), 2996–3012.
[49]
Weichen Zhang, Wanli Ouyang, Wen Li, and Dong Xu. 2018. Collaborative and adversarial network for unsupervised domain adaptation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 3801–3809.
[50]
Wenming Zheng, Xiaoyan Zhou, Cairong Zou, and Li Zhao. 2006. Facial expression recognition using kernel canonical correlation analysis (KCCA). IEEE Transactions on Neural Networks 17, 1 (2006), p.233–238.
[51]
P. Zhou, C. Bai, J. Xia, and S. Chen. 2020. CMRDF: A real-time food alerting system based on multimodal data. IEEE Internet of Things Journal (2020), 1–1. https://doi.org/10.1109/JIOT.2020.2996009
[52]
Xiaowei Zhou, Can Yang, and Weichuan Yu. 2013. Moving object detection by detecting contiguous outliers in the low-rank representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 35, 3 (2013), 597–610.
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November 2021
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Publication History
Published: 12 November 2021
Accepted: 01 March 2021
Revised: 01 March 2021
Received: 01 June 2020
Published in TOMM Volume 17, Issue 4
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- National Key Research and Development Program of China
- National Natural Science Foundation of China
- Primary Research and Development Plan of Jiangsu Province
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