Cascaded Low Rank and Sparse Representation on Grassmann Manifolds
Authors: Boyue Wang, Yongli Hu, Junbin Gao, Yanfeng Sun, Baocai Yin
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results demonstrate the proposed method has excellent performance compared with state-of-the-art clustering methods. |
| Researcher Affiliation | Academia | 1Beijing Advanced Innovation Center for Future Internet Technology, China 2Beijing Key Laboratory of Multimedia and Intelligent Software Technology, China 3Faculty of Information Technology, Beijing University of Technology, China 4The University of Sydney Business School, University of Sydney, NSW 2006, Australia 5Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, China |
| Pseudocode | Yes | Algorithm 1 The G-CLRSR Optimizing Problem. |
| Open Source Code | No | The paper does not explicitly state that the source code for the proposed method (CLRSR/G-CLRSR) is open-source or provide a link to a code repository. |
| Open Datasets | Yes | Extended Yale B dataset... CMUPIE 1 dataset... UCF sport dataset 2... SKIG gesture dataset 3... Highway traffic dataset 4... (Footnotes provide URLs: 1http://www.cs.cmu.edu/afs/cs/project/PIE/Multi Pie/Multi Pie/Home.html, 2http://crcv.ucf.edu/data/, 3http://lshao.staff.shef.ac.uk/data/Sheffield Kinect Gesture.htm, 4http://www.svcl.ucsd.edu/projects/traffic/) |
| Dataset Splits | No | The paper mentions using standard datasets for experiments but does not provide specific details on how these datasets were split into training, validation, or test sets (e.g., percentages, sample counts, or citations to specific predefined splits). |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments, such as GPU/CPU models, memory, or cloud computing specifications. |
| Software Dependencies | No | The paper does not provide specific software dependencies or their version numbers that would be needed to replicate the experiments. |
| Experiment Setup | Yes | The parameters λ1, λ2 and λ3 in CLRSR and G-CLRSR, one parameter λ1 in LRR, SSC, FGLRR and SLRR, two parameters λ1 and λ2 in Lap FGLRR, G-LRSR and LS3C, need to be tuned, respectively. We tune their values within the set of {0.1, 0.2, , 1, 2, 10}, and we report the best value in each experiment. |