Optimal Ridge Detection using Coverage Risk

Part of Advances in Neural Information Processing Systems 28 (NIPS 2015)

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Yen-Chi Chen, Christopher R. Genovese, Shirley Ho, Larry Wasserman


We introduce the concept of coverage risk as an error measure for density ridge estimation.The coverage risk generalizes the mean integrated square error to set estimation.We propose two risk estimators for the coverage risk and we show that we can select tuning parameters by minimizing the estimated risk.We study the rate of convergence for coverage risk and prove consistency of the risk estimators.We apply our method to three simulated datasets and to cosmology data.In all the examples, the proposed method successfully recover the underlying density structure.