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.
- [1] . 2006. A kernel method for canonical correlation analysis. arXiv: Learning (2006).Google Scholar
- [2] . 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/rs9040337Google Scholar
Cross Ref
- [3] . 2013. Deep canonical correlation analysis. In International Conference on Machine Learning.
PMLR , 1247–1255. Google ScholarDigital Library
- [4] . 2019. A generalized multi-dictionary least squares framework regularized with multi-graph embeddings. Pattern Recognition 90 (2019), 1–11. Google Scholar
Cross Ref
- [5] . 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. Google Scholar
Cross Ref
- [6] . 1998. Combining labeled and unlabeled data with co-training. In Proceedings of the Eleventh Annual Conference on Computational Learning Theory. 92–100. Google Scholar
Digital Library
- [7] . 2018. Multi-view low-rank sparse subspace clustering. Pattern Recognition 73 (2018), 247–258.Google Scholar
Cross Ref
- [8] . 2007. Spectral regression for efficient regularized subspace learning. In Proceedings of ICCV, 1–8. https://doi.org/10.1109/ICCV.2007.4408855Google Scholar
- [9] . 2011. Robust principal component analysis. J. ACM 58, 3 (2011), 11. Google Scholar
Digital Library
- [10] . 2012. The convex geometry of linear inverse problems. Foundations of Computational Mathematics 12, 6 (2012), 805–849. Google Scholar
Digital Library
- [11] . 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. Google Scholar
Digital Library
- [12] . 2016. Robust multi-view subspace learning through dual low-rank decompositions. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 30. Google Scholar
Digital Library
- [13] . 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.Google Scholar
Cross Ref
- [14] . 2010. The Pascal visual object classes (VOC) challenge. International Journal of Computer Vision 88, 2 (2010), 303–338. Google Scholar
Digital Library
- [15] . 2013. Multi-view clustering via joint nonnegative matrix factorization. In Proceedings of the 2013 SIAM International Conference on Data Mining.
SIAM , 252–260.Google ScholarCross Ref
- [16] . 2015. Domain generalization for object recognition with multi-task autoencoders. In Proceedings of the IEEE International Conference on Computer Vision. 2551–2559. Google Scholar
Digital Library
- [17] . 2007. Caltech-256 object category dataset. (2007).Google Scholar
- [18] . 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.Google Scholar
- [19] . 2011. Sparse canonical correlation analysis. Machine Learning 83, 3 (2011), 331–353. Google Scholar
Digital Library
- [20] . 2004. Canonical correlation analysis: An overview with application to learning methods. Neural Computation 16, 12 (2004), 2639–2664. Google Scholar
Digital Library
- [21] . 1936. Relations between two sets of variates. Biometrika 28 (1936), 321–377.Google Scholar
Cross Ref
- [22] . 2017. Deeper, broader and artier domain generalization. In Proceedings of the IEEE International Conference on Computer Vision. 5542–5550.Google Scholar
Cross Ref
- [23] . 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.2017.2704624Google Scholar
Cross Ref
- [24] . 2013. Robust recovery of subspace structures by low-rank representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 35, 1 (2013), 171–184. Google Scholar
Digital Library
- [25] , et al. 2010. Robust subspace segmentation by low-rank representation. In Icml, Vol. 1.Citeseer, 8. Google Scholar
Digital Library
- [26] . 2014. Enhancing low-rank subspace clustering by manifold regularization. IEEE Transactions on Image Processing 23, 9 (2014), 4022–4030.Google Scholar
Cross Ref
- [27] . 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.Google Scholar
Cross Ref
- [28] . 2015. Smoothed low rank and sparse matrix recovery by iteratively reweighted least squares minimization. IEEE Transactions on Image Processing 24, 2 (2015), 646–654.Google Scholar
Digital Library
- [29] . 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. Google Scholar
Digital Library
- [30] . 2010. Adapting visual category models to new domains. In European Conference on Computer Vision.
Springer , 213–226. Google ScholarDigital Library
- [31] . 2015. A unified multiset canonical correlation analysis framework based on graph embedding for multiple feature extraction. Neurocomputing 148 (2015), 397–408.Google Scholar
Cross Ref
- [32] . 2020. A generalized least-squares approach regularized with graph embedding for dimensionality reduction. Pattern Recognition 98 (2020), 107023. Google Scholar
Digital Library
- [33] . 2002. The CMU pose, illumination, and expression (PIE) database. In Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.
IEEE , 53–58. Google ScholarDigital Library
- [34] . 2008. A least squares formulation for canonical correlation analysis. In Proceedings of the 25th International Conference on Machine Learning. 1024–1031. Google Scholar
Digital Library
- [35] . 2011. Robust co-training. International Journal of Pattern Recognition and Artificial Intelligence 25, 07 (2011), 1113–1126.Google Scholar
Cross Ref
- [36] . 2003. A framework for robust subspace learning. International Journal of Computer Vision 54, 1-3 (2003), 117–142. Google Scholar
Digital Library
- [37] . 2014. Low rank subspace clustering (LRSC). Pattern Recognition Letters 43 (2014), 47–61.Google Scholar
Cross Ref
- [38] . 2007. Analyzing co-training style algorithms. In European Conference on Machine Learning.
Springer , 454–465. Google ScholarDigital Library
- [39] . 2014. Robust face recognition with structurally incoherent low-rank matrix decomposition. IEEE Transactions on Image Processing 23, 8 (2014), 3294–3307.Google Scholar
Cross Ref
- [40] . 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). Google Scholar
Digital Library
- [41] . 2009. A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis. Biostatistics 10, 3 (2009), 515–534.Google Scholar
Cross Ref
- [42] . 2014. Robust multi-view spectral clustering via low-rank and sparse decomposition. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 28. Google Scholar
Digital Library
- [43] . 2013. A survey on multi-view learning. arXiv: Learning (2013).Google Scholar
- [44] . 2015. Multi-view learning with incomplete views. IEEE Transactions on Image Processing 24, 12 (2015), 5812–5825.Google Scholar
Digital Library
- [45] . 2009. A fast algorithm for edge-preserving variational multichannel image restoration. Siam Journal on Imaging Sciences 2, 2 (2009), 569–592. Google Scholar
Digital Library
- [46] . 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.Google Scholar
- [47] . 2013. Classification of big velocity data via cross-domain canonical correlation analysis. In 2013 IEEE International Conference on Big Data.
IEEE , 493–498.Google ScholarCross Ref
- [48] . 2014. Robust (semi) nonnegative graph embedding. IEEE Transactions on Image Processing 23, 7 (2014), 2996–3012.Google Scholar
Cross Ref
- [49] . 2018. Collaborative and adversarial network for unsupervised domain adaptation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 3801–3809.Google Scholar
Cross Ref
- [50] . 2006. Facial expression recognition using kernel canonical correlation analysis (KCCA). IEEE Transactions on Neural Networks 17, 1 (2006), p.233–238. Google Scholar
Digital Library
- [51] . 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.2020.2996009Google Scholar
Cross Ref
- [52] . 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.Google Scholar
Digital Library
Index Terms
Cross-Domain Object Representation via Robust Low-Rank Correlation Analysis
Recommendations
Low-Rank and Sparse Cross-Domain Recommendation Algorithm
Database Systems for Advanced ApplicationsAbstractIn this paper, we propose a novel Cross-Domain Collaborative Filtering (CDCF) algorithm termed Low-rank and Sparse Cross-Domain (LSCD) recommendation algorithm. Different from most of the CDCF algorithms which tri-factorize the rating matrix of ...
Robust subspace segmentation via nonconvex low rank representation
A nonconvex formulation to determine the low rank representation from contaminated data is proposed.We provide a proximal iteratively reweighed algorithm for solving the nonconvex model.The proposed nonconvex model can recover the underlying low rank ...
Recovering Low-Rank and Sparse Matrices via Robust Bilateral Factorization
ICDM '14: Proceedings of the 2014 IEEE International Conference on Data MiningRecovering low-rank and sparse matrices from partial, incomplete or corrupted observations is an important problem in many areas of science and engineering. In this paper, we propose a scalable robust bilateral factorization (RBF) method to recover both ...






Comments