Feature extraction using an unsupervised neural network
Neural Computation
Self-organizing maps
GTM: the generative topographic mapping
Neural Computation
A reinforcement learning approach to online clustering
Neural Computation
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Two topographic maps for data visualisation
Data Mining and Knowledge Discovery
A Projection Pursuit Algorithm for Exploratory Data Analysis
IEEE Transactions on Computers
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Clustering with reinforcement learning
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Global Reinforcement Learning in Neural Networks
IEEE Transactions on Neural Networks
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We extend a reinforcement learning algorithm which has previously been shown to cluster data. Our extension involves creating an underlying latent space with some pre-defined structure which enables us to create a topology preserving mapping. We investigate different forms of the reward function, all of which are created with the intent of merging local and global information, thus avoiding one of the major difficulties with e.g. K-means which is its convergence to local optima depending on the initial values of its parameters. We also show that the method is quite general and can be used with the recently developed method of stochastic weight reinforcement learning [14].