A letter entitled “Gaussian Process Online Learning With a Sparse Data Stream” suggests an approach for extending the infinite-horizon Gaussian processes (IHGPs, [1]) to deal with a sparse data stream. We point out that there is an error in differentiating the discrete algebraic Riccati equation (DARE), which significantly changes the results of the benchmarking study in a sense that the proposed approach using the solution of the Lyapunov equation does not show outperformance against the original IHGP. In this letter, we provide a correction with details and its consequential implication.

中文翻译：

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对“具有稀疏数据流的高斯过程在线学习”的更正

一封题为“具有稀疏数据流的高斯过程在线学习”的信函提出了一种扩展无限水平高斯过程（IHGPs，[1]）来处理稀疏数据流的方法。我们指出，微分离散代数Riccati方程（DARE）时存在错误，从某种意义上说，使用Lyapunov方程解的拟议方法不会显示出优于原始IHGP的性能，这会大大改变基准研究的结果。 。在这封信中，我们提供了更正，包括细节及其后果。