A fast online learning algorithm of radial basis function network with locality sensitive hashing

In this paper, we propose a new incremental learning algorithm of radial basis function (RBF) Network to accelerate the learning for large-scale data sequence. Along with the development of the internet and sensor technologies, a time series of large data chunk are continuously generated in our daily life. Thus it is usually difficult to learn all the data within a short period. A remedy for this is to select only essential data from a given data chunk and provide them to a classifier model to learn. In the proposed method, only data in untrained regions, which correspond to a region with a low output margin, are selected. The regions are formed by grouping the data based on their near neighbor using locality sensitive hashing (LSH), in which LSH has been developed to search neighbors quickly in an approximated way. As the proposed method does not use all training data to calculate the output margins, the time of the data selection is expected to be shortened. In the incremental learning phase, in order to suppress catastrophic forgetting, we also exploit LSH to select neighbor RBF units quickly. In addition, we propose a method to update the hash table in LSH so that the data selection can be adaptive during the learning. From the performance of nine datasets, we confirm that the proposed method can learn large-scale data sequences fast without sacrificing the classification accuracies. This fact implies that the data selection and the incremental learning work effectively in the proposed method.