Multi-Scale Batch-Learning Growing Neural Gas Efficiently for Dynamic Data Distributions

Growing neural gas (GNG) has many applications, including topology preservation, feature extraction, dynamic adaptation, clustering, and dimensionality re-duction. These methods have broad applicability in extracting the topological structure of 3D point clouds, enabling unsupervised motion estimation, and depict-ing objects within a scene. Furthermore, multi-scale batch-learning GNG (MS-BL-GNG) has improved learning convergence. However, it is only implemented on static or stationary datasets, and adapting to dynamic data remains difficult. Similarly, the learning rate cannot be increased if new nodes are added to the existing network after accumulating errors in the sam-pling data. Next, we propose a new growth approach that, when applied to MS-BL-GNG, significantly in-creases the learning speed and adaptability of dynamic data distribution input patterns. This method imme-diately adds data samples as new nodes to existing net-works. The probability of adding a new node is determined by the distance between the first, second, and third closest nodes. We applied our method for monitoring a moving object at its pace to demonstrate the usefulness of the proposed model. In addition, optimization methods are used such that processing can be performed in real-time.