Drift Correction Using Maximum Independence Domain Adaptation

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.

[1]  Rama Chellappa,et al.  Visual Domain Adaptation: A survey of recent advances , 2015, IEEE Signal Processing Magazine.

[2]  João Gama,et al.  A survey on concept drift adaptation , 2014, ACM Comput. Surv..

[3]  David Zhang,et al.  Improving the transfer ability of prediction models for electronic noses , 2015 .

[4]  David Zhang,et al.  Learning Domain-Invariant Subspace Using Domain Features and Independence Maximization , 2016, IEEE Transactions on Cybernetics.

[5]  Zohreh Azimifar,et al.  Supervised principal component analysis: Visualization, classification and regression on subspaces and submanifolds , 2011, Pattern Recognit..

[6]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[7]  Ming Shao,et al.  Generalized Transfer Subspace Learning Through Low-Rank Constraint , 2014, International Journal of Computer Vision.

[8]  Kilian Q. Weinberger,et al.  Marginalized Denoising Autoencoders for Domain Adaptation , 2012, ICML.

[9]  B. Scholkopf,et al.  Fisher discriminant analysis with kernels , 1999, Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468).

[10]  Bernhard Schölkopf,et al.  Nonlinear Component Analysis as a Kernel Eigenvalue Problem , 1998, Neural Computation.

[11]  Xuelong Li,et al.  Flowing on Riemannian Manifold: Domain Adaptation by Shifting Covariance , 2014, IEEE Transactions on Cybernetics.

[12]  Mikhail Belkin,et al.  Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples , 2006, J. Mach. Learn. Res..

[13]  Le Song,et al.  Colored Maximum Variance Unfolding , 2007, NIPS.

[14]  Kristen Grauman,et al.  Learning Kernels for Unsupervised Domain Adaptation with Applications to Visual Object Recognition , 2014, International Journal of Computer Vision.

[15]  Yuan Shi,et al.  Information-Theoretical Learning of Discriminative Clusters for Unsupervised Domain Adaptation , 2012, ICML.

[16]  Le Song,et al.  Feature Selection via Dependence Maximization , 2012, J. Mach. Learn. Res..

[17]  Tinne Tuytelaars,et al.  Unsupervised Visual Domain Adaptation Using Subspace Alignment , 2013, 2013 IEEE International Conference on Computer Vision.

[18]  Shuzhi Sam Ge,et al.  Drift Compensation for Electronic Nose by Semi-Supervised Domain Adaption , 2014, IEEE Sensors Journal.

[19]  John Blitzer,et al.  Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification , 2007, ACL.

[20]  Bernhard Schölkopf,et al.  Measuring Statistical Dependence with Hilbert-Schmidt Norms , 2005, ALT.

[21]  Ivor W. Tsang,et al.  Domain Adaptation via Transfer Component Analysis , 2009, IEEE Transactions on Neural Networks.

[22]  K. Müller,et al.  Finding stationary subspaces in multivariate time series. , 2009, Physical review letters.

[23]  Yuan Shi,et al.  Geodesic flow kernel for unsupervised domain adaptation , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[24]  David Zhang,et al.  Design of a Breath Analysis System for Diabetes Screening and Blood Glucose Level Prediction , 2014, IEEE Transactions on Biomedical Engineering.

[25]  David Zhang,et al.  Correcting Instrumental Variation and Time-Varying Drift: A Transfer Learning Approach With Autoencoders , 2016, IEEE Transactions on Instrumentation and Measurement.

[26]  Hal Daumé,et al.  Frustratingly Easy Domain Adaptation , 2007, ACL.

[27]  David Zhang,et al.  Calibration transfer and drift compensation of e-noses via coupled task learning , 2016 .

[28]  Min Jiang,et al.  Integration of Global and Local Metrics for Domain Adaptation Learning Via Dimensionality Reduction , 2017, IEEE Transactions on Cybernetics.