Manifold Data Imputation
For researchers working on manifold learning or data imputation, this provides a principled approach to handle missing data in non-Euclidean settings, though the novelty is incremental as it adapts existing functional reconstruction techniques to manifolds.
The paper tackles missing data reconstruction on smooth manifolds from incomplete samples. It proposes a framework combining Fourier-based and variational methods, achieving accurate and stable recovery on surfaces with large gaps without requiring global parameterization.
We consider the problem of reconstructing missing data on a smooth manifold from incomplete and nonuniform samples. While classical methods for manifold approximation typically assume quasi-uniform data, their performance deteriorates significantly in the presence of large gaps or holes. We propose a unified framework for manifold data imputation that reduces the problem to function reconstruction on locally defined tangent spaces. The approach combines two complementary strategies. The first is a Fourier-based method that determines missing values by prescribing a decay rate of the discrete Fourier coefficients, thereby enforcing high-order smoothness through a global spectral criterion. The second is a local variational method based on minimizing high-order central differences, leading to sparse least-squares systems with favorable stability and conditioning properties. We establish a discrete inverse estimate linking decay of Fourier coefficients to uniform bounds on high-order divided differences, providing a theoretical foundation for the spectral approach. For the variational method, we analyze existence, uniqueness, and scaling behavior, showing that conditioning depends primarily on the geometry of the missing region. These functional reconstruction techniques are integrated with a moving least-squares projection framework to yield a practical algorithm for manifold completion. Numerical experiments, including reconstruction on surfaces with significant missing regions, demonstrate accurate and stable recovery without requiring a global parameterization. The proposed framework provides a flexible and effective approach to manifold data imputation in challenging settings with incomplete data.