Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
On Convergence of Epanechnikov Mean Shift
Authors: Kejun Huang, Xiao Fu, Nicholas Sidiropoulos
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show surprisingly good performance compared to the Lloyd s K-means algorithm and the EM algorithm. Illustrative Example Specifically, we test the performance of the proposed deflation-based Epanechnikov Mean Shift and some classic clustering methods, including Lloyd s K-means algorithm, Expectation Maximization (EM) for Gaussian mixture models (GMM), the two-round variant of EM by (Dasgupta and Schulman 2000), and the original Epanechnikov Mean Shift. |
| Researcher Affiliation | Academia | Kejun Huang University of Minnesota Minneapolis, MN 55414 EMAIL Xiao Fu Oregon State University Corvallis, OR 97331 EMAIL Nicholas D. Sidiropoulos University of Virginia Charlottesville, VA 22904 EMAIL |
| Pseudocode | Yes | Algorithm 1 Epanechnikov Mean Shift, Algorithm 2 Epanechnikov Mean Shift iterates Redux, Algorithm 3 Epanechnikov Mean Shift deflation |
| Open Source Code | No | The paper does not provide an explicit statement about the release of source code or a link to a code repository for the methodology described. |
| Open Datasets | No | The experiments are conducted on a synthetic dataset whose generation process is described, but no public access information (link, DOI, formal citation) is provided for the dataset itself. |
| Dataset Splits | No | The paper describes generating synthetic data and repeating simulations, and mentions 'leave-one-out cross-validation' for tuning a parameter, but does not specify explicit training, validation, and test dataset splits with percentages or sample counts for the main evaluation setup. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper mentions that 'The experiment is conducted in MATLAB', but no specific version numbers for MATLAB or any other software dependencies are provided. |
| Experiment Setup | Yes | For d = 100, we prescribe K = 30 clusters (Gaussian components). For cluster k, we first randomly generate its centroid μk N(0, 4I), and then generate Mk = 50k i.i.d. data points from N(μk, I). The procedure is repeated 30 times. The only parameter (kernel bandwidth) is tuned by leave-one-out cross-validation. |