Semi-Supervised Regression and System Identification
Lennart Ljung, Linköping University
Abstract:
System Identification and Machine Learning are developing mostly as independent subjects, although the underlying problem is the same: To be able to associate “outputs” with “inputs”. Particular areas in machine learning of substantial current interest are manifold learning and unsupervised and semi-supervised regression. We outline a general approach to semi-supervised regression, describe its links to Local Linear Embedding, and illustrate its use for various problems. In particular, we discuss how these techniques have a potential interest for the system identification world.