Towards Multi-Mode Outlier Robust Tensor Ring Decomposition
Authors: Yuning Qiu, Guoxu Zhou, Andong Wang, Zhenhao Huang, Qibin Zhao
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | 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. |