Abstract
Transfer samples are required by the drift correction algorithms in the last three chapters. When transfer samples are not available, we can resort to unsupervised domain adaptation approaches. Maximum independence domain adaptation (MIDA) is proposed in this chapter for unsupervised drift correction. MIDA borrows the definition of domain features in the last chapter and learns features which have maximal independence with them, so as to reduce the inter-domain discrepancy in distributions. A feature augmentation strategy is designed so that the learned subspace is background-specific. Semi-supervised MIDA (SMIDA) extends MIDA by exploiting the label information. The proposed algorithms are flexible and fast. The effectiveness of our approaches is verified by experiments on synthetic datasets and three real-world ones on sensors and measurement.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Barshan E, Ghodsi A, Azimifar Z, Jahromi MZ (2011) Supervised principal component analysis: visualization, classification and regression on subspaces and submanifolds. Pattern Recogn 44(7):1357–1371
Belkin M, Niyogi P, Sindhwani V (2006) Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. J Mach Learn Res 7:2399–2434
Blitzer J, Dredze M, Pereira F (2007) Biographies, bollywood, boom-boxes and blenders: domain adaptation for sentiment classification. ACL 7:440–447
Chen M, Xu Z, Weinberger K, Sha F (2012) Marginalized denoising autoencoders for domain adaptation. In: 29th international conference on machine learning
Cui Z, Li W, Xu D, Shan S, Chen X, Li X (2014) Flowing on riemannian manifold: domain adaptation by shifting covariance. IEEE Trans Cybern 44(12):2264–2273
Daum III H (2007) Frustratingly easy domain adaptation. In: Proceedings of 45th annual meeting of the association for computational linguistics
Fernando B, Habrard A, Sebban M, Tuytelaars T (2013) Unsupervised visual domain adaptation using subspace alignment. In: Proceedings of the IEEE international conference on computer vision, pp 2960–2967
Gama J, Žliobaite I, Bifet A, Pechenizkiy M, Bouchachia A (2014) A survey on concept drift adaptation. ACM Comput Surv (CSUR) 46(4):44
Gong B, Shi Y, Sha F, Grauman K (2012) Geodesic flow kernel for unsupervised domain adaptation. In: 2012 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 2066–2073
Gong B, Grauman K, Sha F (2014) Learning kernels for unsupervised domain adaptation with applications to visual object recognition. Int J Comput Vis 109(1–2):3–27
Gretton A, Bousquet O, Smola A, Schlkopf B (2005) Measuring statistical dependence with hilbert-schmidt norms. In: Algorithmic learning theory. Springer, pp 63–77
Jiang M, Huang W, Huang Z, Yen G (2016) Integration of global and local metrics for domain adaptation learning via dimensionality reduction. IEEE Trans Cybern
Liu Q, Li X, Ye M, Ge SS, Du X (2014) Drift compensation for electronic nose by semi-supervised domain adaption. IEEE Sens J 14(3):657–665
Pan SJ, Yang Q (2010) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345–1359
Pan SJ, Tsang IW, Kwok JT, Yang Q (2011) Domain adaptation via transfer component analysis. IEEE Trans Neural Netw 22(2):199–210
Patel VM, Gopalan R, Li R, Chellappa R (2015) Visual domain adaptation: a survey of recent advances. IEEE Signal Process Mag 32(3):53–69
Schlkopf B, Smola A, Mller KR (1998) Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput 10(5):1299–1319
Scholkopft B, Mullert KR (1999) Fisher discriminant analysis with kernels. Neural Netw Signal Process IX:41–48
Shao M, Kit D, Fu Y (2014) Generalized transfer subspace learning through low-rank constraint. Int J Comput Vis 109(1–2):74–93
Shi Y, Sha F (2012) Information-theoretical learning of discriminative clusters for unsupervised domain adaptation. In: Proceedings of the international conference on machine learning (ICML)
Song L, Gretton A, Borgwardt KM, Smola AJ (2007) Colored maximum variance unfolding. In: Advances in neural information processing systems, pp 1385–1392
Song L, Smola A, Gretton A, Bedo J, Borgwardt K (2012) Feature selection via dependence maximization. J Mach Learn Res 13(1):1393–1434
Von Bünau P, Meinecke FC, Király FC, Müller KR (2009) Finding stationary subspaces in multivariate time series. Phys Rev Lett 103(21):214,101
Yan K, Zhang D (2015) Improving the transfer ability of prediction models for electronic noses. Sens Actuators B: Chem 220:115–124
Yan K, Zhang D (2016a) Calibration transfer and drift compensation of e-noses via coupled task learning. Sens Actuators B: Chem 225:288–297
Yan K, Zhang D (2016b) Correcting instrumental variation and time-varying drift: a transfer learning approach with autoencoders. IEEE Trans Instrum Meas 65(9):2012–2022
Yan K, Zhang D, Wu D, Wei H, Lu G (2014) Design of a breath analysis system for diabetes screening and blood glucose level prediction. IEEE Trans Biomed Eng 61(11):2787–2795
Yan K, Kou L, Zhang D (2017) Learning domain-invariant subspace using domain features and independence maximization. IEEE Trans Cybern
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2017 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Zhang, D., Guo, D., Yan, K. (2017). Drift Correction Using Maximum Independence Domain Adaptation. In: Breath Analysis for Medical Applications. Springer, Singapore. https://doi.org/10.1007/978-981-10-4322-2_9
Download citation
DOI: https://doi.org/10.1007/978-981-10-4322-2_9
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-4321-5
Online ISBN: 978-981-10-4322-2
eBook Packages: Computer ScienceComputer Science (R0)