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..
Towards Multi-Mode Outlier Robust Tensor Ring Decomposition
Authors: Yuning Qiu, Guoxu Zhou, Andong Wang, Zhenhao Huang, Qibin Zhao
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experimental Results In this section, we evaluate the performance of the proposed approach by conducting experiments on both synthetic data and real-world datasets, including light field images and hyperspectral videos. We compare the experimental results with some state-of-the-art robust matrix/tensor decomposition approaches... |
| Researcher Affiliation | Academia | 1 School of Automation, Guangdong University of Technology, Guangzhou, 510006, China 2 RIKEN Center for Advanced Intelligence Project, Tokyo, 1030027, Japan 3 Key Laboratory of Intelligent Detection and The Internet of Things in Manufacturing, Ministry of Education, Guangzhou, 510006, China |
| Pseudocode | No | The optimization algorithm employed to solve the Eq. (5) hinges on the utilization of the Alternating Direction Method of Multipliers (ADMM) algorithm (Boyd et al. 2011). For a comprehensive understanding, please refer to Appendix B. |
| Open Source Code | Yes | The implementation code is available at https://github.com/ynqiu/MORTRD. |
| Open Datasets | Yes | We randomly select four HSV datasets2. ... https://www.hsitracking.com/contest/ We adopt a publicly accessible light field images dataset3, and randomly select 6 of these images. ... https://lightfield-analysis.uni-konstanz.de/ |
| Dataset Splits | No | No explicit specification of training, validation, and test dataset splits (e.g., percentages, sample counts, or cross-validation setup) was found in the main text. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, processor types, memory amounts) used for running experiments were provided in the paper. |
| Software Dependencies | No | The paper mentions the use of the Alternating Direction Method of Multipliers (ADMM) algorithm but does not specify any software libraries or dependencies with version numbers. |
| Experiment Setup | Yes | To generate synthetic low rank tensor T P Rd1ˆd2ˆ ˆd K with TR rank rr1, r2, , r Ks, we first generated K core tensors Gpkq P Rrkˆdkˆrk 1 where each entry is produced by the i.i.d. Gaussian distribution Np0, 1q. To construct the latent structural tensor Sk, , we let the support set of Sk pkq be Ωk, and then randomly select |Ωk pkq| columns of Sk pkq as outliers whose entries obey i.i.d. Np0, 1q. Thus, the outlier is given by S řK k 1 Sk. The additive noise tensor is produced by Np0, σ2q, where σ 10 3}T }F{ ? D to guarantee a constant signal-to-noise ratio (SNR). All the experiments are repeated 10 times and their mean values are reported. ... The additive Gaussian noise is set as Section 5.1 with σ 0.05}T }F{ ? D. The multi-mode outliers are generated with |Ωk pkq| roundp10 2 ś j,j k djq, and each outlier Sk pkqp:, iq is generated by a uniform discrete distribution on r 1, 1s. |