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..
Robust Multi-View Subspace Learning through Dual Low-Rank Decompositions
Authors: Zhengming Ding, Yun Fu
AAAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on two multi-view benchmarks, e.g., face and object images, have witnessed the superiority of our proposed algorithm, by comparing it with the state-of-the-art algorithms. |
| Researcher Affiliation | Academia | Department of Electrical & Computer Engineering, Northeastern University, Boston, USA College of Computer & Information Science, Northeastern University, Boston, USA EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Solution to Problem (3) |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of their methodology. |
| Open Datasets | Yes | CMU-PIE Face database is consisted of 68 subjects in total, which is a multi-view face dataset 1 and show large variances within the same subject but in different poses. ... 1http://vasc.ri.cmu.edu/idb/html/face/. COIL-100 object database2 contains 100 categories with 7200 images. ... 2http://www.cs.columbia.edu/CAVE/software/softlib/coil100.php |
| Dataset Splits | No | The paper describes training and testing splits, but does not explicitly mention a separate validation set or provide details for a validation split. |
| Hardware Specification | Yes | Experiments are conducted with Matlab 2014b, CPU i7-3770 and 32 GB memory size. |
| Software Dependencies | Yes | Experiments are conducted with Matlab 2014b, CPU i7-3770 and 32 GB memory size. |
| Experiment Setup | Yes | In Algorithm 1, where we set those parameters μ0, ρ, ϵ, tmax and μmax empirically, while tuning the two trade-offs, i.e., λ and α throughout the experiment, which is further discussed in experimental part. Therefore, we set λ = 10 2 and α = 102 throughout the experiments. |