Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Tensor-SVD Based Graph Learning for Multi-View Subspace Clustering
Authors: Quanxue Gao, Wei Xia, Zhizhen Wan, Deyan Xie, Pu Zhang3930-3937
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results reveal that our WTNNM method is superior to several state-of-the-art multi-view subspace clustering methods in terms of performance. |
| Researcher Affiliation | Academia | Quanxue Gao,1 Wei Xia,1 Zhizhen Wan,1 Deyan Xie,1 Pu Zhang1 1State Key Laboratory of Integrated Services Networks, Xidian University, Xi an 710071, China. |
| Pseudocode | Yes | Algorithm 1 WTNNM for Multi-view clustering |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described (e.g., no specific repository link, explicit code release statement, or code in supplementary materials). |
| Open Datasets | Yes | Yale1 face database has 165 grayscaling images of 15 persons. Just as (Luo et al. 2018), we extract three types of features: 4096 dimensions intensity feature, 3304 LBP feature, and 6750 dimensions Gabor feature. 1http://vision.ucsd.edu/content/yale-face-database and Caltech-101 database2 contains 8677 images of objects belonging to 101 categories... 2http://www.vision.caltech.edu/Image Datasets/Caltech101/ |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | Yes | Parameter Setting In our experiments, we tune the parameter λ in the range of [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 5, 10, 100], and the weight ωi (0, 100] to get the best results. Specifically, λ is set to 1, the weight vector ω is set to (1; 10; 100) on Yale dataset; λ is set to 0.5, the weight vector ω is set to (1; 10; 100) on Notting-Hill dataset; λ is set to 0.01, the weight vector ω is set to (0.5; 5; 10) on Caltech-101 dataset; λ is set to 0.01, the weight vector ω is set to (1; 10; 100) on UCI-Digits dataset; λ is set to 0.005, the weight vector ω is set to (0.5; 1; 5) on Scene-15 dataset. |