Incremental Unsupervised Time Series Analysis Using Merge Growing Neural Gas

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Self-organizing neural integration of pose-motion features for human action recognition (2015, background): “For instance, Andreakis et al. (2009) devised a recursive GNG network with a context layer to learn spatio-temporal patterns.”
Human motion assessment in real time using recurrent self-organization (2016, methodology): “We compared the performance of our MGWR on a time series analysis task with other two well-established models of recursive self-organization: Merge Neural Gas (MNG) [15] and Merge Growing Neural Gas (MGNG) [17].”
Nonlinear Time Series Analysis by Using Gamma Growing Neural Gas (2012, methodology): “As this type of context does not depend on the lattice architecture, it can be combined with other self-organizing neural networks such as Neural Gas (NG) [5] and Growing Neural Gas (GNG) [6], to produce the Merge Neural Gas (MNG) [7] and the Merge Growing Neural Gas (MGNG) [8], respectively.”
Gamma-Filter Self-Organizing Neural Networks for Time Series Analysis (2011, methodology): “For the GNG model we use the implementation proposed in [ 11 ], which incorporates a node insertion criterion based on entropy maximization.”