Optimal Ridge Detection using Coverage Risk
Authors: Yen-Chi Chen, Christopher R. Genovese, Shirley Ho, Larry Wasserman
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 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. In all simulations, our selection rule allows the SCMS algorithm to detect the underlying structure of the data. |
| Researcher Affiliation | Academia | Yen-Chi Chen Department of Statistics Carnegie Mellon University yenchic@andrew.cmu.edu Christopher R. Genovese Department of Statistics Carnegie Mellon University genovese@stat.cmu.edu Shirley Ho Department of Physics Carnegie Mellon University shirleyh@andrew.cmu.edu Larry Wasserman Department of Statistics Carnegie Mellon University larry@stat.cmu.edu |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | We apply our method to three simulated datasets and to cosmology data. We apply the proposed method to several famous datasets including the spiral dataset, the three spirals dataset, and the NIPS dataset. Now we apply our technique to the Sloan Digital Sky Survey, a huge dataset that contains millions of galaxies. |
| Dataset Splits | Yes | The second approach is to use data splitting. We randomly split the data into X 11, , X 1m and X 21, , X 2m, assuming n is even and 2m = n. We compute the estimated manifolds by using half of the data, which we denote as b R 1,n and b R 2,n. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, processor types, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions the use of "Subspace Constrained Mean Shift (SCMS) algorithm" and "kernel density estimator (KDE)" but does not specify any software names with version numbers. |
| Experiment Setup | Yes | Therefore, the SCMS algorithm requires a preselected parameter h, which acts as the role of smoothing bandwidth in the kernel density estimator. Having estimated the risk, we select h by h = argmin h hn d Risk 1,n. Note that we also remove the ridges whose density is below 0.05 maxx bpn(x) since they behave like random noise. |