Efficient Multi-Dimensional Tensor Sparse Coding Using t-Linear Combination
Authors: Fei Jiang, Xiao-Yang Liu, Hongtao Lu, Ruimin Shen
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on multi-dimensional signals denoising and reconstruction (3DTSC, 4DTSC, 5DTSC) show that the proposed algorithms are more efficient and outperform the state-of-theart tensor-based sparse coding models. |
| Researcher Affiliation | Academia | 1Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China 2Department of Electrical Engineering, Columbia University, New York, NY10027, USA Emails: jiangf@alumni.sjtu.edu.cn; xiaoyang@ee.columbia.edu; htlu@sjtu.edu.cn; rmshen@sjtu.edu.cn |
| Pseudocode | Yes | Algorithm 1 Algorithms for the MDTSC. ... Algorithm 2 Tensor-based Fast Iterative Shrinkage Thresholding Algorithm (TFISTA) |
| Open Source Code | No | The paper does not include an unambiguous statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We evaluate the performance of the proposed MDTSC schemes using 3D MSI images in Columbia MSI database (Wang et al. 2004)1 by adding two kinds of commonly existing noises in MSIs. ... 1http://www1.cs.columbia.edu/CAVE/databases/multispectral/ |
| Dataset Splits | No | The paper does not explicitly state specific training, validation, and test splits (e.g., percentages or sample counts) for the datasets used beyond general descriptions of data usage for dictionary learning and testing. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU/CPU models, memory) used to run its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | The size of the dictionary is set to 64x256x10, the same as (Zhang and Aeron 2016). ... The size of the dictionary is set to 5x10x5x5. ... The size of the dictionary is set to 8x16x8x3x10. ... We add Guassian white noise at different noise levels σ = 5, 10, 20, 30, 50. |