We present xokde++, a state-of-the-art online kernel density estimation approach that maintains Gaussian mixture models input data streams. The approach follows state-of-the-art work on online density estimation, but was redesigned with computational efficiency, numerical robustness, and extensibility in mind. Our approach produces comparable or better results than the current state-of-the-art, while achieving significant computational performance gains and improved numerical stability. The use of diagonal covariance Gaussian kernels, which further improve performance and stability, at a small loss of modelling quality, is also explored. Our approach is up to 40 times faster, while requiring 90\% less memory than the closest state-of-the-art counterpart.

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• Computer and Information Sciences - Natural Sciences
• Electrical Engineering, Electronic Engineering, Information Engineering - Engineering and Technology