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Optimal detection of the feature matching map in presence of noise and outliers. (English) Zbl 07633946

Summary: 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 detected 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 may 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 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 the 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 algorithms studied in this work.

MSC:

62H12 Estimation in multivariate analysis
62F35 Robustness and adaptive procedures (parametric inference)
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