Generalized Higher-Order Tensor Decomposition via Parallel ADMM
Authors: Fanhua Shang, Yuanyuan Liu, James Cheng
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results verify that our regularized formulation is effective, and our methods are robust to noise or outliers. In this section, we evaluate both the effectiveness and efficiency of our methods for solving tensor decomposition problems using both synthetic and real-world data. All experiments were performed on an Intel(R) Core (TM) i5-4570 (3.20 GHz) PC running Windows 7 with 8GB main memory. |
| Researcher Affiliation | Academia | 1Department of Computer Science and Engineering, The Chinese University of Hong Kong 2Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong |
| Pseudocode | Yes | Algorithm 1 Solving problem (5) via parallel ADMM; Algorithm 2 Solving problem (11) via Parallel ADMM |
| Open Source Code | No | The paper does not provide any explicit statement about releasing its source code or a link to a code repository. |
| Open Datasets | Yes | MRI Data This part compares our CTD and NCTD methods, HOSVD and HOOI on a 181 217 181 brain MRI data used in (Liu et al., 2009). |
| Dataset Splits | No | The paper describes how synthetic data was generated and mentions a real-world MRI dataset, but it does not specify explicit training/validation/test splits or percentages. |
| Hardware Specification | Yes | All experiments were performed on an Intel(R) Core (TM) i5-4570 (3.20 GHz) PC running Windows 7 with 8GB main memory. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., programming languages, libraries, or frameworks with their versions). |
| Experiment Setup | Yes | We set the regularization parameter λ = 100 for our methods. Other parameters of Mixture, ADMM, HOSVD and HOOI are set to their default values. We set the tensor ranks Rn = 1.2r , n = 1, . . . , N and Tol = 10 5 for all these algorithms. Initialize: Y 0 n = 0, G0 n = 0, U 0 n = rand(In, Rn), µ0 = 10 4, µmax = 1010, ρ = 1.05, and Tol = 10 5. |