Learning with Adaptive Neighbors for Image Clustering
Authors: Yang Liu, Quanxue Gao, Zhaohua Yang, Shujian Wang
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | 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. |