Does Gaussian Process Regression (GPR) have computational complexity?
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Gaussian Process Regression (GPR) has a high computational complexity, which has been a long-standing challenge. The standard GPR requires the inversion of a kernel matrix, resulting in a cubic time complexity growth with the number of samples . This high computational complexity hinders the wide application of GPR . However, there have been several approaches proposed to address this issue. One approach is to approximate the covariance kernel using eigenvalues and functions, leading to a significant reduction in training and regression complexity . Another approach is to sequentially partition the input space and fit a localized Gaussian process to each disjoint region, resulting in superior time and space complexity compared to existing methods . Additionally, an online compression scheme has been developed to preserve convergence to the population posterior while reducing the computational burden with streaming data .
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2 Citations | The paper does not explicitly mention the computational complexity of Gaussian Process Regression (GPR). |
| The paper does not explicitly mention the computational complexity of Gaussian Process Regression (GPR). | |
| The paper states that Gaussian process regression suffers from poor scaling in both memory and computational complexity. | |
5 Citations | The paper states that Gaussian processes have a computational burden that scales cubically with the training sample size. |
| The paper discusses the computational complexity of Gaussian process regression (GPR) and proposes a method to reduce the complexity. |
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