Rank Determination for Low-Rank Data Completion
Authors: Morteza Ashraphijuo, Xiaodong Wang, Vaneet Aggarwal
JMLR 2017 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Furthermore, we provide numerical experiments for the case of single-view matrix, where we use nuclear norm minimization to find a low-rank completion of the sampled data and we observe that in most of the cases the proposed upper bound on the rank is equal to the true rank. |
| Researcher Affiliation | Academia | Morteza Ashraphijuo EMAIL Columbia University New York, NY 10027, USA Xiaodong Wang EMAIL Columbia University New York, NY 10027, USA Vaneet Aggarwal EMAIL Purdue University West Lafayette, IN 47907, USA |
| Pseudocode | No | The paper describes methods like nuclear norm minimization and tensor decompositions but does not present them in structured pseudocode or algorithm blocks. The procedural descriptions are integrated into the main text. |
| Open Source Code | No | The paper mentions using a "Matlab program found online (Shabat, 2015)" and "Matlab toolbox found online Tensorlab" for specific parts of their experiments. However, it does not provide any specific link or explicit statement about releasing their own implementation code for the methodology described. |
| Open Datasets | No | Here, we first generate an n1 n2 random matrix of a given rank r by multiplying a random (entries are drawn according to a uniform distribution on real numbers within an interval) n1 r matrix and r n2 matrix. Then, each entry of the randomly generated matrix is sampled uniformly at random and independently across entries with some sampling probability p. |
| Dataset Splits | No | The paper describes generating random matrices and sampling their entries for experimental evaluation, but it does not specify explicit training, validation, or test dataset splits. The data is generated on the fly for each run. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments, such as CPU or GPU models, or memory specifications. |
| Software Dependencies | No | The paper mentions using a "Matlab program found online (Shabat, 2015)" and the "Matlab toolbox found online Tensorlab". However, it does not provide specific version numbers for Matlab, the program from Shabat, or Tensorlab, which are necessary for reproducible software dependencies. |
| Experiment Setup | No | The paper describes the objective function for nuclear norm minimization (equation 17) and how data is generated and sampled (e.g., varying rank 'r' and sampling probability 'p'). However, it does not provide specific hyperparameter values (like learning rates, batch sizes, number of epochs), optimizer settings, or other detailed configuration steps for a training process. |