Accelerated Bayesian optimization for CNN+LSTM learning rate tuning via precomputed Gaussian process subspaces in soil analysis - Chen, Xiaolong; Zhang, Hongfeng; Wong, Cora Un In; Song, Zhengchun (Frontiers in Environmental Science, 2025)
PurposeWe propose an accelerated Bayesian optimization framework for tuning the learning rate of CNN+LSTM models in soil analysis, addressing the computational inefficiency of traditional Gaussian Process (GP)-based methods. This work bridges the gap between computational efficiency and probabilistic robustness, with broader implications for automated machine learning in geoscientific applications.MethodThe key innovation lies in a subspace-accelerated GP surrogate model that precomputes low-rank approximations of covariance matrices offline, thereby decoupling the costly hyperparameter tuning from the online acquisition function evaluations. By projecting the hyperparameter search space onto a dominant subspace derived from Nyström approximations, our method reduces the computational complexity from cubic to linear in the number of observations. The proposed system integrates seamlessly with existing CNN+LSTM pipelines, where the offline phase constructs the GP subspace using historical or synthetic data, while the online phase iteratively updates the subspace with rank-1 modifications. Moreover, the method’s adaptability to non-stationary response surfaces, facilitated by a Matérn-5/2 kernel with automatic relevance determination, makes it particularly suitable for soil data exhibiting multi-scale features.ResultsEmpirical validation on soil spectral datasets demonstrates a 3–5× speedup in convergence compared to standard Bayesian optimization, with no loss in model accuracy. Experiments on soil spectral datasets show convergence in 23.4 min (3.8× faster than standard Bayesian optimization) with a test RMSE of 0.142, while maintaining equivalent accuracy across diverse CNN+LSTM architectures.ConclusionThe reformulated approach not only overcomes the scalability limitations of conventional GP-based optimization but also preserves its theoretical guarantees, offering a practical solution for hyperparameter tuning in resource-constrained environments.