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..
Learning with Adaptive Neighbors for Image Clustering
Authors: Yang Liu, Quanxue Gao, Zhaohua Yang, Shujian Wang
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results illustrate that the proposed model outperforms other state-of-the-art clustering algorithms. |
| Researcher Affiliation | Academia | Yang Liu1, Quanxue Gao1 , Zhaohua Yang2 , Shujian Wang1 1 State Key Laboratory of Integrated Services Networks, Xidian University, Xi an, China 2 Beihang University, Beijing, China |
| Pseudocode | Yes | Algorithm 1: Input: affinity matrix A Rn n, cluster number c, parameter γ. |
| Open Source Code | No | The paper does not provide any explicit statement or link for the release of source code for the proposed methodology. |
| Open Datasets | Yes | COIL20 databast [Nene et al., 1996] includes 1440 color images of 20 objects (72 images per object). UMIST dataset [Graham and Allinson, 1998] consists of 564 images of 20 individuals... Handwritten numerals (HW) dataset [Asuncion and Newman, 2007] is composed of 2,000 data points for 0 to 9 ten digit classes... MSRC-v1 dataset [Winn and Jojic, 2005] contains 240 images and can be divided into 8 classes. |
| Dataset Splits | No | The paper describes using benchmark datasets but does not provide specific details on training, validation, or test set splits, such as percentages or sample counts for each partition. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory specifications) used for conducting the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependency details, such as library names with version numbers, required to replicate the experiment. |
| Experiment Setup | Yes | In our model, we determine the value of γ in a heuristic way to accelerate the procedure. At first, we set γ with a small positive value, then in each iteration, decrease it ( γ = γ/2 ) if the number of zero eigenvalues in Ls is larger than class number c or increase it ( γ = 2γ ) if smaller than c, otherwise the iteration stopped. For each dataset, we repeat experiments 10 times because all the methods are spectral clustering based methods. In general, m < 10 can produce good results. |