Principal Component Analysis in Space Forms

TL;DR

This paper extends Principal Component Analysis to space forms with constant curvature, proposing a new method that offers faster convergence and better accuracy for manifold-valued data in spherical and hyperbolic spaces.

Contribution

It introduces Space Form PCA (SFPCA), a novel approach with cost functions leading to eigensolutions and nested subspaces, improving over existing iterative methods.

Findings

SFPCA outperforms existing methods in convergence speed.

SFPCA achieves higher accuracy in estimating true subspaces.

Effective on both real and simulated data in spherical and hyperbolic spaces.

Abstract

Principal Component Analysis (PCA) is a workhorse of modern data science. While PCA assumes the data conforms to Euclidean geometry, for specific data types, such as hierarchical and cyclic data structures, other spaces are more appropriate. We study PCA in space forms; that is, those with constant curvatures. At a point on a Riemannian manifold, we can define a Riemannian affine subspace based on a set of tangent vectors. Finding the optimal low-dimensional affine subspace for given points in a space form amounts to dimensionality reduction. Our Space Form PCA (SFPCA) seeks the affine subspace that best represents a set of manifold-valued points with the minimum projection cost. We propose proper cost functions that enjoy two properties: (1) their optimal affine subspace is the solution to an eigenequation, and (2) optimal affine subspaces of different dimensions form a nested set. These properties provide advances over existing methods, which are mostly iterative algorithms with slow convergence and weaker theoretical guarantees. We evaluate the proposed SFPCA on real and simulated data in spherical and hyperbolic spaces. We show that it outperforms alternative methods in estimating true subspaces (in simulated data) with respect to convergence speed or accuracy, often both.