Abstract
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.
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Fyfe, C., Lai, P.L. (2007). Reinforcement Learning Reward Functions for Unsupervised Learning. In: Liu, D., Fei, S., Hou, ZG., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4491. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72383-7_47
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DOI: https://doi.org/10.1007/978-3-540-72383-7_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72382-0
Online ISBN: 978-3-540-72383-7
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