Iterated geometric harmonics for data imputation and reconstruction of missing data
This work addresses data imputation for incomplete datasets, particularly in image reconstruction, but appears incremental as it adapts an existing method to handle missing data.
The paper tackles the problem of reconstructing missing data in high-dimensional datasets, such as damaged images with up to 70% data loss, by proposing an iterated geometric harmonics (IGH) scheme that converges to near-optimal solutions within 4-6 iterations and runtimes under 30 minutes on a desktop computer.
The method of geometric harmonics is adapted to the situation of incomplete data by means of the iterated geometric harmonics (IGH) scheme. The method is tested on natural and synthetic data sets with 50--500 data points and dimensionality of 400--10,000. Experiments suggest that the algorithm converges to a near optimal solution within 4--6 iterations, at runtimes of less than 30 minutes on a medium-grade desktop computer. The imputation of missing data values is applied to collections of damaged images (suffering from data annihilation rates of up to 70\%) which are reconstructed with a surprising degree of accuracy.