Authors: (1) Sha-Bo Lin, Center for Intelligent Decision-Making and Machine Learning, School of Management, Xi’an Jiaotong University; (2) Xingping Sun, Department of Mathematics, Missouri State University; (3) Di Wang, §Center for Intelligent Decision-Making and Machine Learning, School of Management, Xi’an Jiaotong University. Content Overview Abstract & Introduction Uncertainty Relation of Kernel Interpolation on Spheres Distributed Kernel Interpolation Operator Differences via Quadrature Rules Proofs Numerical Verifications References Abstract For radial basis function (RBF) kernel interpolation of scattered data, Schaback [30] in 1995 proved that the attainable approximation error and the condition number of the underlying interpolation matrix cannot be made small simultaneously. He referred to this finding as an “uncertainty relation”, an undesirable consequence of which is that RBF kernel interpolation is susceptible to noisy data. In this paper, we propose and study a distributed interpolation method to manage and quantify the uncertainty brought on by interpolating noisy spherical data of non-negligible magnitude. We also present numerical simulation results showing that our method is practical and robust in terms of handling noisy data from challenging computing environments. Kernel interpolation, distributed uncertainty mitigation, scattered spherical data Key words. Uncertainty Relation of Kernel Interpolation on Spheres 3. Distributed Kernel Interpolation. Corollary 2.2 shows that kernel interpolation performs poorly while confronting noisy data of non-negligible magnitude. To overcome this major drawback, we propose and study in this section a distributed kernel interpolation (DKI) method, which is motivated by the “distributed learning” in the literature [37, 19]. Figuratively speaking, this is a divide-and-conquer strategy for uncertainty quantification. To elaborate, we describe the method in three steps. 4. Operator Differences via Quadrature Rules. In this section, we first briefly exposit an integral-operator approach initiated in [8] and then derive tight upper bounds for differences of operators of our interest, obtaining a certain type of Sobolev sampling inequalities [12] as a byproduct. Highlights of the section include Proposition 4.5) and Lemma 4.8. 6. Numerical Verifications Four simulations are carried out in this section to verify the excellent performance of DKI. The first one shows that DKI succeeds in circumventing the uncertainty of kernel interpolation. The second one exhibits the role of m in DKI. The third one focuses on the role of the division strategy in DKI. The last one compares DKI with several popular spherical data fitting schemes including the distributed filtered hyperinterpolation (DFH) [21], sketching with s ∗ -designs [20], and distributed kernel ridge regression (DKRR) [8]. In this simulation, we show the role of the parameter m in DKI. We generate 10014 training samples (with 141-designs as inputs). The number of divisions, m, ranges from {5, 10, · · · , 200}. Figure 6.2 shows the relation between RMSE of DKI and the number of local machines under different levels of Gaussian noise, provided that the total number of training samples is given. From Figure 6.2, we can conclude the following assertions: 1) For training samples with higher levels of noise, the testing RMSE generally decreases at first and then increases slowly as the number of local machines increases. Moderate values of m are more conductive to good approximation property for DKI. The reason is that too small m does not successfully address the uncertainty issue in kernel interpolation; too large m increases the fitting error, resulting in slightly worse generalization performance. 2) The optimal number m with the lowest RMSE grows with increasing Gaussian noise. This verifies the equation (3.3) of Theorem 3.2, in which the approximation error is primarily concerned with the sample error for large noise (i.e., large M) and can be reduced using a large m. Simulation 2: REFERENCES [1] R. Bhatia, Matrix Analysis, vol. 169, Springer Science & Business Media, 2013. [2] J. S. Brauchart and K. Hesse, Numerical integration over spheres of arbitrary dimension, Constructive Approximation, 25 (2007), pp. 41–71. [3] G. Brown and F. Dai, Approximation of smooth functions on compact two-point homogeneous spaces, Journal of Functional Analysis, 220 (2005), pp. 401–423. [4] A. Chernih, I. H. Sloan, and R. S. Womersley, Wendland functions with increasing smoothness converge to a gaussian, Advances in Computational Mathematics, 40 (2014), pp. 185– 200. [5] F. Dai, Multivariate polynomial inequalities with respect to doubling weights and a∞ weights, Journal of Functional Analysis, 235 (2006), pp. 137–170. [6] F. 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Phillips, Potential anomalies on a sphere: Applications to the thickness of the lunar crust, Journal of Geophysical Research: Planets, 103 (1998), pp. 1715–1724. [36] R. S. Womersley, Efficient spherical designs with good geometric properties, in Contemporary computational mathematics-A celebration of the 80th birthday of Ian Sloan, Springer, 2018, pp. 1243–1285. [37] Y. Zhang, J. Duchi, and M. Wainwright, Divide and conquer kernel ridge regression: A distributed algorithm with minimax optimal rates, The Journal of Machine Learning Research, 16 (2015), pp. 3299–3340. In this Appendix, we present the detailed implementation of the select-and-judge (SAJ) strategy. Our aim is to derive a series of subsets of similar Cardinality with separation radius not smaller than a given tolerance c0. There are two stages for SAJ. SM1. Appendix A: Select-and-Judge Strategy for Data Division. This paper is under CC0 1.0 DEED license. available on arxiv

Distributed Uncertainty Quantification of Kernel Interpolation on Spheres

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