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Self-taught learning is a technique that uses a large number of unlabeled data as source samples to improve the task performance on target samples.
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A. Rozantsev, M. Salzmann, P. Fua, Beyond Sharing Weights for Deep Domain Adaptation, IEEE Trans. Pattern Anal. Mach. Intell. 41 (4) (2018) 801–814
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R. Shang, Y. Meng, C. Liu, L. Jiao, A. M. G. Esfahani, R. Stolkin, Unsupervised Feature Selection Based on Kernel Fisher Discriminant Analysis and Regression Learning, Mach. Learn. 108 (4) (2019) 659–686
2019
Closest in time.