Guaranteed Tensor Decomposition: A Moment Approach
Authors: Gongguo Tang, Parikshit Shah
ICML 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments are conducted to test the performance of the moment approach. We performed a series of experiments to illustrate the performance of the SDP relaxations (9) in solving the tensor decomposition and other related problems. |
| Researcher Affiliation | Collaboration | Gongguo Tang GTANG@MINES.EDU Colorado School of Mines, 1500 Illinois Street, Golden, CO, USA Parikshit Shah PSHAH@DISCOVERY.WISC.EDU Yahoo! Labs, 701 First Ave, Sunnyvale CA, USA |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | No | The paper describes generating synthetic data for experiments (e.g., 'We produced T = 10 instances', 'A set of r random, orthonormal vectors... were generated to produce the tensor A') but does not specify or provide access information for any publicly available or open dataset. |
| Dataset Splits | No | The paper does not explicitly provide information about train/validation/test dataset splits. It discusses generating synthetic data and sampling observations for completion tasks, but not in terms of distinct training, validation, and testing sets. |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware used for running the experiments. It does not mention CPU, GPU, or memory specifications. |
| Software Dependencies | No | The paper mentions 'All the SDPs are solved using the CVX package.' but does not specify a version number for CVX or any other software dependencies. |
| Experiment Setup | Yes | In preparing the upper plot in Figure 1, we took n = 10, r 2 {2, 4, . . . , 20}, and 2 {0.38, 0.39, . . . , 0.52}. Gaussian noise of standard deviation σ equal to half the average magnitude of the tensor elements was added to all the unique entries of the tensor. We then ran the optimization (22) with k = 2 to perform denoising. The penalization parameter γ is set to equal σ. |