We develop a fully Bayesian nonparametric regression model based on a L\'evy process prior named MLABS (Multivariate L\'evy Adaptive B-Spline regression) model, a multivariate version of the LARK models, for obtaining an elaborate estimation of unknown functions with either varying degrees of smoothness or high interaction orders. L\'evy process priors have advantages of encouraging sparsity in the expansions and providing automatic selection over the number of basis functions. The unknown regression function is expressed as a weighted sum of tensor product of B-spline basis functions as the elements of an overcomplete system, which can deal with multi-dimensional data. The B-spline basis can express systematically functions with varying degrees of smoothness. By changing a set of degrees of the tensor product basis function, MLABS can adapt the smoothness of target functions due to the nice properties of B-spline bases. The local support of the B-spline basis enables the MLABS to make more delicate predictions than other existing methods in the two-dimensional surface data. For practice, we apply the structure of tensor products bases of (Bayesian) MARS to the MLABS model to reduce the computation burden. Experiments on various simulated and real-world datasets illustrate that the MLABS model has comparable performance on regression and classification problems. We also show that the MLABS model has more stable and accurate predictive abilities than state-of-the-art nonparametric regression models in relatively low-dimensional data.
翻译:我们开发了一个完全的巴伊斯非参数回归模型,其基础是L\'evy 进程,先前命名的MALBS(Multistriate L\'evy 适应性B-Spline回归)模型,这是LARK模型的多变量版本,目的是获得对未知功能的详细估计,其平滑度或互动量不同。L\'evy进程前的优点在于鼓励扩展中的宽度,并为基础功能的数量提供自动选择。未知回归函数表现为一个加权和B-spline基础函数的成份,作为超完整的系统元素,可以处理多维数据。BS-spline基础可以以不同程度的平滑度表示系统性功能。通过改变成一套高压产品基功能的度,LAMBS可以调整目标功能的平滑度。BS基础的本地支持使得我们比二维表面数据中的其他现有方法更精确的回溯性预测值。关于实践,我们应用不精确的系统模型的系统化功能,可以以不同的平滑度表示出不同程度的模型的功能模型。通过可比较性BS-BS的模型的模型的模型的模型,可以将S-modal-modal-al-al-moxxx 的模型的模型的模型的模型的模型的模型的模拟的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型和模型的模拟的模型的模拟的模拟的模拟的模拟的模拟的模拟的模拟的模拟的模拟的模拟的模型的模拟的模型的模型的模型的模型的模型的模拟性能的模拟性能演示。