Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Joint Capped Norms Minimization for Robust Matrix Recovery
Authors: Feiping Nie, Zhouyuan Huo, Heng Huang
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The evaluation experiments are performed on both synthetic data and real world applications in collaborative filtering and social network link prediction. All empirical results show our new method outperforms the existing matrix recovery methods. |
| Researcher Affiliation | Academia | Feiping Nie1, Zhouyuan Huo2, Heng Huang2 1School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xian 710072, Shaanxi, P. R. China. 2Computer Science and Engineering, University of Texas at Arlington, USA. |
| Pseudocode | Yes | The proposed algorithm to solve problem (6) is described in Alg. 1. |
| Open Source Code | No | The paper does not provide concrete access to source code, such as a repository link or an explicit statement about code release for the methodology. |
| Open Datasets | Yes | We evaluate our method on the following data sets: The Jester Jokes data sets [Goldberg et al., 2001]1, Wikipedia [Leskovec et al., 2009] and Epinions [Massa and Avesani, 2006] data sets2 and Sweetrs data set3. ... 1http://eigentaste.berkeley.edu/dataset/ 2http://snap.stanford.edu/data/ 3http://sweetrs.org |
| Dataset Splits | Yes | In our experiments, all the parameters are selected via 5-fold cross validation to guarantee their best performance. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., libraries, frameworks, or solvers). |
| Experiment Setup | Yes | In our experiments, all the parameters are selected via 5-fold cross validation to guarantee their best performance. ... In reference to our objective function, there are three parameters to tune, ε1, ε2 and γ. Fig. 1 shows the relationship between ε1, ε2 and γ with performance evaluation metric n MAE. In the experiment, we fix two parameters and change another one. |