Nonparametric ridge estimation. (English) Zbl 1310.62045

Summary: We study the problem of estimating the ridges of a density function. Ridge estimation is an extension of mode finding and is useful for understanding the structure of a density. It can also be used to find hidden structure in point cloud data. We show that, under mild regularity conditions, the ridges of the kernel density estimator consistently estimate the ridges of the true density. When the data are noisy measurements of a manifold, we show that the ridges are close and topologically similar to the hidden manifold. To find the estimated ridges in practice, we adapt the modified mean-shift algorithm proposed by U. Ozertem and D. Erdogmus [J. Mach. Learn. Res. 12, 1249–1286 (2011; Zbl 1280.62071)]. Some numerical experiments verify that the algorithm is accurate.


62G05 Nonparametric estimation
62G07 Density estimation
62G20 Asymptotic properties of nonparametric inference
62H12 Estimation in multivariate analysis


Zbl 1280.62071
Full Text: DOI arXiv Euclid


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