Automatic Model Selection in Subspace Clustering via Triplet Relationships
Authors: Jufeng Yang, Jie Liang, Kai Wang, Yong-Liang Yang, Ming-Ming Cheng
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on the benchmark datasets demonstrate the effectiveness and robustness of the proposed approach. |
| Researcher Affiliation | Academia | 1College of Computer and Control Engineering, Nankai University, No.38 Tongyan Road, Tianjin, China 2Department of Computer Science, University of Bath, Claverton Down, Bath, United Kingdom |
| Pseudocode | Yes | Algorithm 1 : Automatic Subspace Clustering (auto SC) |
| Open Source Code | No | The paper does not provide any links to open-source code or explicitly state that the code for their methodology is publicly available. |
| Open Datasets | Yes | In the experiments, we first compare the proposed auto SC with various automatic methods on two benchmark datasets, i.e., the extended Yale B and the COIL-20 dataset. |
| Dataset Splits | No | The paper mentions using benchmark datasets and evaluating performance but does not provide specific details on how the data was split into training, validation, or test sets (e.g., percentages, counts, or cross-validation scheme). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments (e.g., GPU models, CPU types, memory). |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers required to reproduce the experiments. |
| Experiment Setup | No | The paper states, "All parameters of the contrasted methods are tuned to be the best," but does not provide specific hyperparameter values or detailed system-level training settings for reproducibility. |