Dynamic matrix recovery from incomplete observations under an exact low-rank constraint
Authors: Liangbei Xu, Mark Davenport
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To empirically verify our analysis, we perform both synthetic and real world experiments, described in Section 5. The synthetic experimental results demonstrate that LOWEMS outperforms the naïve approach in practice both in terms of recovery accuracy and sample complexity. We also demonstrate the effectiveness of LOWEMS in the context of recommendation systems. |
| Researcher Affiliation | Academia | Liangbei Xu Mark A. Davenport Department of Electrical and Computer Engineering Georgia Institute of Technology Atlanta, GA 30318 lxu66@gatech.edu mdav@gatech.edu |
| Pseudocode | No | The paper describes the alternating minimization algorithm in text, but no structured pseudocode or algorithm block is provided. |
| Open Source Code | No | The paper does not provide any explicit statements about making the source code available or include a link to a code repository for the methodology described. |
| Open Datasets | No | The paper mentions using 'the (truncated) Netflix dataset' but does not provide a specific link, DOI, repository name, or formal citation with authors and year for accessing this dataset. |
| Dataset Splits | Yes | We keep the latest (in time) 10% of the ratings as a testing set. The remaining ratings are split into a validation set and a training set for the purpose of cross validation. We divide the remaining ratings into d {1, 3, 6, 8} bins respectively with same time period according to their timestamps. We use 5-fold cross validation, and we keep 1/5 of the ratings from the dth bin as a validation set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory, or cloud instance specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies, such as library names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | We set n1 = 100, n2 = 50, d = 4 and r = 5. We set the measurement noise level σ1 to 0.05. We vary the perturbation noise level σ2. The number of latent factors r is set to 10. The Frobenius norm regularization parameter γ is set to 1. We also note that in practice one likely has no prior information on σ1, σ2 and hence κ. However, we use model selection techniques like cross validation to select the best κ incorporating the unknown prior information on measurement/perturbation noise. |