Linearity-Aware Subspace Clustering
Authors: Yesong Xu, Shuo Chen, Jun Li, Jianjun Qian8770-8778
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental Results" section, as well as discussing "Clustering Performance and Analysis" and presenting data in "Table 2: Performance comparison of all compared methods on the five benchmark datasets." |
| Researcher Affiliation | Academia | 1PCA Lab , Nanjing University of Science and Technology yesong xu@163.com, {shuochen, junli, csjqian}@njust.edu.cn |
| Pseudocode | Yes | Algorithm 1: Subspace Segmentation via LASC |
| Open Source Code | No | The paper does not provide an explicit statement or a link to open-source code for the described methodology. |
| Open Datasets | Yes | Datasets: In our experiments, five benchmark datasets are selected, including two handwritten digits datasets MNIST1 and USPS2, two object recognition datasets COIL1003 and CIFAR104, and an face image dataset Extended Yale B(Ex Yale B)5. (with URLs provided in footnotes) |
| Dataset Splits | Yes | For all above mentioned algorithms, the parameters are tuned by the cross validation technique to guarantee their possibly optimal performance. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as CPU or GPU models, or memory specifications. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python version, specific libraries like PyTorch or TensorFlow with their versions) that would be needed to replicate the experiment. |
| Experiment Setup | Yes | Table 2 presents the clustering results and the tuned parameters of all tested approaches. For example, for SSC on MNIST with 6 classes, the parameters are '0.001, 0.1'. The paper also states 'Initialize: C0 = S0 = 0 and ε1 = 10 4, ε2 = 10 5' and mentions a 'maximal iteration number T'. |