Optimal detection of the feature matching map in presence of noise and outliers
This addresses a fundamental challenge in feature matching for computer vision or data analysis, providing theoretical guarantees for robust matching in noisy, high-dimensional settings, though it is incremental in refining existing bounds.
The paper tackles the problem of detecting a matching map between two sets of vectors with noise and outliers, showing that consistent estimation is possible when inlier-inlier distances are at least order d^{1/4} and inlier-outlier distances at least order d^{1/2} in high dimensions, with optimality proven via lower bounds.
We consider the problem of finding the matching map between two sets of $d$ dimensional vectors from noisy observations, where the second set contains outliers. The matching map is then an injection, which can be consistently estimated only if the vectors of the second set are well separated. The main result shows that, in the high-dimensional setting, a detection region of unknown injection can be characterized by the sets of vectors for which the inlier-inlier distance is of order at least $d^{1/4}$ and the inlier-outlier distance is of order at least $d^{1/2}$. These rates are achieved using the estimated matching minimizing the sum of logarithms of distances between matched pairs of points. We also prove lower bounds establishing optimality of these rates. Finally, we report results of numerical experiments on both synthetic and real world data that illustrate our theoretical results and provide further insight into the properties of the estimators studied in this work.