Towards Discriminant Analysis Classifiers Using Online Active Learning via Myoelectric Interfaces
Keywords:Machine Learning (ML)
AbstractWe propose a discriminant analysis (DA) classifier that uses online active learning to address the need for the frequent training of myoelectric interfaces due to covariate shift. This online classifier is initially trained using a small set of examples, and then updated over time using streaming data that are interactively labeled by a user or pseudo-labeled by a soft-labeling technique. We prove, theoretically, that this yields the same model as training a DA classifier via full batch learning. We then provide experimental evidence that our approach improves the performance of DA classifiers and is robust to mislabeled data, and that our soft-labeling technique has better performance than existing state-of-the-art methods. We argue that our proposal is suitable for real-time applications, as its time complexity w.r.t. the streaming data remains constant.
How to Cite
Jaramillo-Yanez, A. G., Benalcázar, M. E., Sardina, S., & Zambetta, F. (2022). Towards Discriminant Analysis Classifiers Using Online Active Learning via Myoelectric Interfaces. Proceedings of the AAAI Conference on Artificial Intelligence, 36(6), 6996-7004. https://doi.org/10.1609/aaai.v36i6.20658
AAAI Technical Track on Machine Learning I