Abstract
In this chapter, we propose drift correction autoencoder (DCAE) to deal with instrumental variation and time-varying drift of e-noses . DCAE learns to model and correct these influential factors explicitly with the help of transfer samples . It generates drift-corrected and discriminative representation of the original data, which can then be applied to various prediction algorithms. Experimental results show that DCAE outperforms typical drift correction algorithms and autoencoder-based transfer learning methods. In particular, it is better than TMTL in the last chapter in datasets with complex drift, at the cost of longer training time and more hyper-parameters.
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Zhang, D., Guo, D., Yan, K. (2017). A Transfer Learning Approach for Correcting Instrumental Variation and Time-Varying Drift. In: Breath Analysis for Medical Applications. Springer, Singapore. https://doi.org/10.1007/978-981-10-4322-2_8
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