k-variates++: more pluses in the k-means++
Authors: Richard Nock, Raphael Canyasse, Roksana Boreli, Frank Nielsen
ICML 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the applicability of our analysis via experimental evaluation on several domains and settings, displaying competitive performances vs state of the art. Section 5 presents experimental results. |
| Researcher Affiliation | Collaboration | Data61, Sydney, Australia & { The Australian National University; The University of New South Wales} Ecole Polytechnique, Palaiseau, France & { The Technion, Haifa, Israel; Sony CS Labs, Inc., Tokyo, Japan} |
| Pseudocode | Yes | Algorithm 0 k-variates++ ... Algorithm 1 Dk-means++ ... Algorithm 2 Sk-means++ ... Algorithm 3 OLk-means++ |
| Open Source Code | No | The paper refers to a Supplementary Information (Nock et al., 2016a) for proofs and extensive experiments, but does not provide a direct link to any open-source code repository for the methodology. |
| Open Datasets | No | The paper mentions datasets like 'Life Sci', 'Image', 'Europe Diff' in Table 2 and 'synthetic data', but does not provide concrete access information (e.g., specific links, DOIs, or citations with authors/year) for public availability. |
| Dataset Splits | No | The paper does not explicitly provide training/validation/test dataset splits, percentages, or cross-validation details needed to reproduce the experiment. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU/CPU models, memory details) used to run its experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for ancillary software components used in the experiments. |
| Experiment Setup | Yes | Parameters are in line with (Bahmani et al., 2012). ... We let ϵ = 1 in our experiments. ... each peer s initial data consists of points uniformly sampled in a random hyperrectangle in a space of d = 50 (expected number of peers points mi = 500, i). We sample peers until a total of m 20000 point is sampled. |