This paper proposes a Gaussian process-based self-organizing incremental neural network (GPINN) to address the density estimation problem of online unsupervised learning. First, we adopt Gaussian process models with adaptive kernels that map the distribution of the neighbors of each node to its link relationship. Second, combining GPINN and kernel density estimation, we derive the bandwidth matrix updating rule for adapting to the generated network. We theoretically analyze the advantages of the proposed approach in determining threshold regions over using distance measures. The experimental results on both synthetic data sets and real-world data sets show that our method achieves remarkable improvement in density estimation accuracy for large noisy data.

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