Curvature Corrected Nonnegative Manifold Data Factorization
This work addresses the need for geometry-aware data analysis methods in scientific domains like medical imaging, but it appears incremental as an adaptation of existing factorizations for manifold-valued data.
The paper tackled the problem of extracting interpretable factors from manifold-valued data with nonlinear structure by proposing curvature corrected nonnegative manifold data factorization (CC-NMDF), and demonstrated the method on real-world diffusion tensor MRI data.
Data with underlying nonlinear structure are collected across numerous application domains, necessitating new data processing and analysis methods adapted to nonlinear domain structure. Riemannanian manifolds present a rich environment in which to develop such tools, as manifold-valued data arise in a variety of scientific settings, and Riemannian geometry provides a solid theoretical grounding for geometric data analysis. Low-rank approximations, such as nonnegative matrix factorization (NMF), are the foundation of many Euclidean data analysis methods, so adaptations of these factorizations for manifold-valued data are important building blocks for further development of manifold data analysis. In this work, we propose curvature corrected nonnegative manifold data factorization (CC-NMDF) as a geometry-aware method for extracting interpretable factors from manifold-valued data, analogous to nonnegative matrix factorization. We develop an efficient iterative algorithm for computing CC-NMDF and demonstrate our method on real-world diffusion tensor magnetic resonance imaging data.