Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
A Unified Framework for Discrete Spectral Clustering
Authors: Yang Yang, Fumin Shen, Zi Huang, Heng Tao Shen
IJCAI 2016 | Venue PDF | 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}. |