Tight Continuous Relaxation of the Balanced k-Cut Problem
Authors: Syama Sundar Rangapuram, Pramod Kaushik Mudrakarta, Matthias Hein
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive comparisons show that our method outperforms all existing approaches for ratio cut and other balanced k-cut criteria. Extensive experiments show that our method outperforms all existing methods in terms of the achieved balanced k-cuts. |
| Researcher Affiliation | Academia | Syama Sundar Rangapuram, Pramod Kaushik Mudrakarta and Matthias Hein Department of Mathematics and Computer Science Saarland University, Saarbr ucken |
| Pseudocode | No | The paper describes the algorithm and its steps through narrative text and mathematical formulations, but it does not include a clearly labeled pseudocode block or algorithm section within the main body. |
| Open Source Code | No | The paper states 'We used the publicly available code [22, 13] with default settings.', which refers to code for other methods used for comparison, not the authors' own implementation. |
| Open Datasets | Yes | In addition to the datasets provided in [13], we also selected a variety of datasets from the UCI repository shown below. For all the datasets not in [13], symmetric k-NN graphs are built with Gaussian weights... Iris wine vertebral ecoli 4moons webkb4 optdigits USPS pendigits 20news MNIST |
| Dataset Splits | No | The paper does not explicitly state specific percentages, sample counts, or a detailed methodology for splitting data into training, validation, and test sets. It mentions 'train' and 'test' in context but does not provide explicit validation split information. |
| Hardware Specification | No | The paper does not explicitly specify any hardware details such as CPU/GPU models, memory, or specific computing environments used for running the experiments. |
| Software Dependencies | No | The paper mentions using specific algorithms like 'PDHG algorithm [15, 16]' and refers to 'publicly available code [22, 13]' for comparative methods, but it does not provide specific software dependencies or version numbers for its own implementation (e.g., programming languages, libraries, or solvers with version numbers). |
| Experiment Setup | Yes | We run our method using 5 random initializations, 7 initializations based on the spectral clustering solution similar to [13] (who use 30 such initializations). In addition to the datasets provided in [13]... We chose the parameters s and k in a method independent way by testing for each dataset several graphs using all the methods over different choices of k {3, 5, 7, 10, 15, 20, 40, 60, 80, 100} and s {0.1, 1, 4}. The best choice in terms of the clustering error across all the methods and datasets, is s = 1, k = 15. |