A novel variational form of the Schatten-$p$ quasi-norm
Authors: Paris Giampouras, Rene Vidal, Athanasios Rontogiannis, Benjamin Haeffele
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, the efficiency of our approach is empirically shown on a matrix completion problem.In this section we provide simulated and real data experimental results that advocate the merits of the proposed variational form of the Sp quasi-norm in the case of the matrix completion problem. |
| Researcher Affiliation | Academia | Paris Giampouras Mathematical Institute for Data Science Johns Hopkins University parisg@jhu.edu René Vidal Mathematical Institute for Data Science Johns Hopkins University rvidal@jhu.edu Athanasios Rontogiannis IAASARS National Observatory of Athens tronto@noa.gr Benjamin D. Haeffele Mathematical Institute for Data Science Johns Hopkins University bhaeffele@jhu.edu |
| Pseudocode | No | The main paper text does not contain pseudocode or a clearly labeled algorithm block. It mentions 'Analytical details of the algorithm are provided in the supplement,' but the supplement is not included in the provided text. |
| Open Source Code | No | The paper does not provide concrete access to source code, such as a specific repository link or an explicit code release statement. |
| Open Datasets | Yes | Real data. We next test the algorithms on the Movie Lens-100K dataset, [24] which contains 100,000 ratings (integer values from 1 to 5) for 943 movies by 1682 users. [24] Movielens dataset. [Online]. Available: https://grouplens.org/datasets/movielens/ |
| Dataset Splits | No | The paper mentions 'missing rates' for the observed entries but does not specify explicit training, validation, or test dataset splits, or cross-validation setup. |
| Hardware Specification | Yes | All experiments are conducted on a Mac Book Pro with 2.6 GHz 6-Core Intel Core i7 CPU and 16GB RAM using MATLAB R2019b. |
| Software Dependencies | Yes | All experiments are conducted on a Mac Book Pro with 2.6 GHz 6-Core Intel Core i7 CPU and 16GB RAM using MATLAB R2019b. |
| Experiment Setup | Yes | For each case we initialize all matrix factorization based algorithms with ranks ranging from 10 to 50 with step-size 5. |