Discrete Multiple Kernel k-means
Authors: Rong Wang, Jitao Lu, Yihang Lu, Feiping Nie, Xuelong Li
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments illustrated the effectiveness and superiority of the proposed model. In this section, we evaluate the clustering performance of the proposed DMKKM model on a number of real-world datasets. |
| Researcher Affiliation | Academia | Rong Wang1 , Jitao Lu2 , Yihang Lu2 , Feiping Nie2 and Xuelong Li2 1School of Cybersecurity and School of Artificial Intelligence, Optics and Electronics (i OPEN), Northwestern Polytechnical University, Xi an 710072, Shaanxi, P. R. China 2School of Computer Science and School of Artificial Intelligence, Optics and Electronics (i OPEN), Northwestern Polytechnical University, Xi an 710072, Shaanxi, P. R. China |
| Pseudocode | Yes | Algorithm 1 Coordinate descent to solve problem (13) and Algorithm 2 The procedure to solve problem (11) |
| Open Source Code | No | The paper mentions that 'The source codes are downloaded from the authors pages or requested from the authors.' but this refers to comparison models and not the authors' own DMKKM implementation. No concrete access to their own source code is provided. |
| Open Datasets | Yes | Seven real-world benchmark datasets are employed to evaluate the clustering performance, including Handwritten, Pima, Protein Fold, Sens ITVehicle, UCI DIGIT, Washington and Wisconsin. All these datasets are downloaded from Xinwang Liu s page1 and more details can be found in their published papers. 1https://xinwangliu.github.io |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | The paper states that DMKKM is 'completely parameter-free' and mentions 'gird search to determine the hyperparameters for all comparison models', but it does not provide specific experimental setup details such as hyperparameter values, training configurations, or system-level settings for their own model. |