The Annals of Statistics

Principal component analysis for functional data on Riemannian manifolds and spheres

Xiongtao Dai and Hans-Georg Müller

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Functional data analysis on nonlinear manifolds has drawn recent interest. Sphere-valued functional data, which are encountered, for example, as movement trajectories on the surface of the earth are an important special case. We consider an intrinsic principal component analysis for smooth Riemannian manifold-valued functional data and study its asymptotic properties. Riemannian functional principal component analysis (RFPCA) is carried out by first mapping the manifold-valued data through Riemannian logarithm maps to tangent spaces around the Fréchet mean function, and then performing a classical functional principal component analysis (FPCA) on the linear tangent spaces. Representations of the Riemannian manifold-valued functions and the eigenfunctions on the original manifold are then obtained with exponential maps. The tangent-space approximation yields upper bounds to residual variances if the Riemannian manifold has nonnegative curvature. We derive a central limit theorem for the mean function, as well as root-$n$ uniform convergence rates for other model components. Our applications include a novel framework for the analysis of longitudinal compositional data, achieved by mapping longitudinal compositional data to trajectories on the sphere, illustrated with longitudinal fruit fly behavior patterns. RFPCA is shown to outperform an unrestricted FPCA in terms of trajectory recovery and prediction in applications and simulations.

Article information

Ann. Statist., Volume 46, Number 6B (2018), 3334-3361.

Received: May 2017
Revised: October 2017
First available in Project Euclid: 11 September 2018

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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 62G05: Estimation
Secondary: 62G20: Asymptotic properties 62G99: None of the above, but in this section

Compositional data dimension reduction functional data analysis functional principal component analysis principal geodesic analysis Riemannian manifold trajectory central limit theorem uniform convergence


Dai, Xiongtao; Müller, Hans-Georg. Principal component analysis for functional data on Riemannian manifolds and spheres. Ann. Statist. 46 (2018), no. 6B, 3334--3361. doi:10.1214/17-AOS1660.

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Supplemental materials

  • Supplement to “Principal component analysis for functional data on Riemannian manifolds and spheres”. In the Supplementary Materials, we provide proofs of Corollary 2, Theorem 2 and Corollary 4; algorithms for RFPCA of compositional data; and additional simulations.