Low-Rank Tensor Completion with Spatio-Temporal Consistency
Authors: Hua Wang, Feiping Nie, Heng Huang
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our method shows promising results in all evaluations on both 3D biomedical image sequence and video benchmark data sets. |
| Researcher Affiliation | Academia | Department of Electrical Engineering and Computer Science Colorado School of Mines, Golden, Colorado 80401, USA Department of Computer Science and Engineering University of Texas at Arlington, Arlington, Texas 76019, USA |
| Pseudocode | Yes | Algorithm 3: Algorithm to solve the problem (9). |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper mentions using "3D MRI images" and "Foreman data and Flower data" but does not provide specific links, DOIs, repository names, or formal citations for public access to these datasets. |
| Dataset Splits | No | The paper describes corrupting data (e.g., removing 70% pixels) to simulate missing values but does not provide specific training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper describes algorithms and methods (e.g., ADM, APG, LRTC, Tucker) but does not provide specific software dependencies with version numbers (e.g., library names with versions). |
| Experiment Setup | Yes | In our experiments, we set the parameter α in Eq. (9) as 1 for simplicity. Given the input tensor X, we empirically set the error bound ε = 0.05 P ijk |xijk|/ (r c n). |