Perturbation-based Learning

Perturbation based methods are proposed under the smoothness assumption, which indicates that two data points close to each other in feature space are likely to have the same label.

References

Zheng, Y., & Song, Y. (2021). An Effective Perturbation-Based Semi-Supervised Learning Method for Acoustic Event Classification. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 29, 3580-3591. Link.

Engelen, S., & Hoos, H. (2020). A survey on semi-supervised learning. Machine Learning, 109(2), 299-337. Link.