We extend a reinforcement learning algorithm, REINFORCE [13] which has previously been used to cluster data [10]. By using base Gaussian learners, we extend the method so that it can perform a variety of unsupervised learning tasks such as principal component analysis, exploratory projection pursuit and canonical correlation analysis. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Fyfe, C., & Lai, P. L. (2007). Reinforcement learning reward functions for unsupervised learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4491 LNCS, pp. 397–402). Springer Verlag. https://doi.org/10.1007/978-3-540-72383-7_47
Mendeley helps you to discover research relevant for your work.