A Unified Framework for Discrete Spectral Clustering
Authors: Yang Yang, Fumin Shen, Zi Huang, Heng Tao Shen
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments conducted on various data sets have demonstrated the superiority of our proposal as compared to existing clustering approaches. |
| Researcher Affiliation | Academia | University of Electronic Science and Technology of China, Chengdu, China The University of Queensland, Brisbane, Australia |
| Pseudocode | Yes | Algorithm 1 Algorithm for optimizing the proposed spectral clustering model. |
| Open Source Code | No | The paper does not provide any statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | We evaluate our proposed approach on six UCI datasets [Lichman, 2013], including Image Segmentation, Vehicle, Vote, Ecoli, Solar and Wine. |
| Dataset Splits | Yes | We randomly choose 50% of samples for training and the rest are used for test. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU types, or memory specifications used for the experiments. |
| Software Dependencies | No | The paper does not specify any software names with version numbers, such as programming languages, libraries, or solvers. |
| Experiment Setup | Yes | We set the number of neighbors k to 5 for all spectral clustering methods. The parameters of all comparison algorithms are tested in {10 6, 10 4, 10 2, 100, 102, 104, 106}. We set p of 2,p loss in {0.25, 0.5, 0.75, 1, 1.25, 1.5, 1.75}. |