Multiple Kernel Clustering Framework with Improved Kernels
Authors: Yueqing Wang, Xinwang Liu, Yong Dou, Rongchun Li
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental research has been conducted on 7 MKC benchmarks. As is shown, our algorithms consistently and significantly improve the performance of the base MKC algorithms, indicating the effectiveness of the proposed framework. |
| Researcher Affiliation | Academia | National Laboratory for Parallel and Distributed Processing, NUDT, Changsha, China, 410073 xinwangliu@nudt.edu.cn |
| Pseudocode | Yes | Algorithm 1: Proposed MKCF-IK. Algorithm 2: Discovering Outliers. Algorithm 3: Proposed CSRC-IK. |
| Open Source Code | No | The paper does not provide explicit statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper lists various datasets (e.g., 'bbcsport', 'YALE', 'protein Fold', 'Caltech102', 'Flower17', 'Digital', 'CCV') used in experiments but does not provide specific links, DOIs, repository names, or formal citations for their public access. |
| Dataset Splits | No | The paper does not explicitly provide specific training, validation, and test dataset splits (e.g., percentages, sample counts, or references to predefined splits). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory, or cloud instance types) used for running the experiments. |
| Software Dependencies | No | The paper mentions 't-SNE [Laurens, 2013]' for visualization but does not provide specific version numbers for any software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | For each center, we collect a proportion of samples with top largest distances to it, and add their indices to Op. We term this proportion as r. In our experiments, this r is set to be 0.05 or 0.1. |