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arXiv · 1606.02608

Fast and Extensible Online Multivariate Kernel Density Estimation

Jaime Ferreira·David Martins de Matos·Ricardo Ribeiro
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Abstract

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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Jaime Ferreira, David Martins de Matos, Ricardo Ribeiro. 2016-06-08. Fast and Extensible Online Multivariate Kernel Density Estimation. https://arxiv.org/abs/1606.02608

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