Unified Graph and Low-Rank Tensor Learning for Multi-View Clustering
Authors: Jianlong Wu, Xingyu Xie, Liqiang Nie, Zhouchen Lin, Hongbin Zha6388-6395
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To evaluate the performance of the proposed method, we conduct extensive experiments on multiple benchmarks across different scenarios and sizes. |
| Researcher Affiliation | Collaboration | 1School of Computer Science and Technology, Shandong University 2Zhejiang Laboratory 3Key Laboratory of Machine Perception (MOE), School of EECS, Peking University 4Samsung Research China Beijing (SRC-B) |
| Pseudocode | Yes | Algorithm 1 Alternating Minimization Method for UGLTL |
| Open Source Code | No | The paper does not provide a statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We adopt six challenging image datasets, which cover various sizes and applications, including the COIL201, UCI-Digits (Asuncion and Newman 2007), Scene15 (Li and Pietro 2005), Notting-Hill (Zhang et al. 2009), MITIndoor-67 (Quattoni and Torralba 2009), and Caltech-101 (Li, Rob, and Pietro 2007) datasets. |
| Dataset Splits | No | The paper mentions using datasets for experiments but does not provide specific details on training, validation, or test splits (e.g., percentages or sample counts for each partition). |
| Hardware Specification | Yes | All experiments are implemented in Matlab on a desktop with 3.4GHz CPU and 32G RAM. |
| Software Dependencies | No | The paper states that experiments are "implemented in Matlab", but does not provide a version number for Matlab or any other specific software dependencies with versions. |
| Experiment Setup | Yes | The dimension M of projected features in the subspace is very small and we set it to M = 8 in our experiments. [...] The parameters α, β, and γ are fine-tuned by searching the grid of {0.01, 0.1, 1, 10, 100}. [...] In experiments, we set τ = 5 on all datasets |