Robust k-means: a Theoretical Revisit
Authors: ALEXANDROS GEORGOGIANNIS
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this work, we present a theoretical analysis of the robustness and consistency properties of a variant of the classical quadratic k-means algorithm, the robust k-means... The synthetic data for the experiments come from a mixture of Gaussians... In Figures 2-3, we plot the results... The results for each scenario (accuracy, cluster estimation error, etc) are averages over 150 runs of the experiment. |
| Researcher Affiliation | Academia | Alexandros Georgogiannis School of Electrical and Computer Engineering Technical University of Crete, Greece alexandrosgeorgogiannis at gmail.com |
| Pseudocode | No | The paper describes algorithms and procedures in prose, but it does not include any structured pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., specific repository link, explicit statement of code release) for the source code of the methodology described. |
| Open Datasets | No | The paper uses synthetic data generated for the experiments ('The synthetic data for the experiments come from a mixture of Gaussians with 10 components...'), but it does not provide concrete access information (link, DOI, formal citation) for a publicly available or open dataset. |
| Dataset Splits | No | The paper describes the synthetic data and experimental setup, but it does not specify explicit training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory, or processor types) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'the R package trimcluster [10]' and 'the R toolbox Mix Sim [14]', but it does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | The parameter a in trimmed k-means (the percentage of outliers) is set to a = 0.3, while the value of the parameter λ for which (RKM) yields 150 outliers is found through a search over a grid on the set λ (0, λmax) (we set λmax as the maximum distance between two points in a dataset)... In Figures 2-3, we plot the results for a proximal map Pf like the one in (16) with h(x) = αx and α = 0.005. |