PU Learning for Matrix Completion
Authors: Cho-Jui Hsieh, Nagarajan Natarajan, Inderjit Dhillon
ICML 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We first use synthetic data to show that our bounds are meaningful and then demonstrate the effectiveness of our algorithms in real world applications. |
| Researcher Affiliation | Academia | Cho-Jui Hsieh CJHSIEH@CS.UTEXAS.EDU Nagarajan Natarajan NAGA86@CS.UTEXAS.EDU Inderjit S. Dhillon INDERJIT@CS.UTEXAS.EDU Department of Computer Science, The University of Texas, Austin, TX 78721, USA |
| Pseudocode | No | The paper describes algorithms and optimization techniques mathematically and textually, but it does not contain a structured pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We use 4 real-world datasets: 2 co-author networks ca-Gr Qc(4,158 nodes and 26,850 edges) and ca Hep Ph(11,204 nodes and 235,368 edges)...; 2 social networks Live Journal(1,770,961 nodes, |Ωtrain| = 83,663,478 and |Ωtest| = 2,055,288) and My Space(2,137,264 nodes, |Ωtrain| = 90,333,122 and |Ωtest| = 1,315,594)...We test the algorithms on two datasets: the Mushroom dataset with 8142 samples, 112 features, and 2 classes; the Segment dataset with 2310 samples, 19 features, and 7 classes. |
| Dataset Splits | Yes | in all the experiments we chose ρ from the set {1 2s, 10(1 2s), 100(1 2s), 1000(1 2s)} based on a random validation set, and use the corresponding α in the optimization problems. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'eigs in MATLAB' but does not specify a version number for MATLAB or any other software libraries or dependencies with their respective versions. |
| Experiment Setup | Yes | We fix ρ = 0.9 (so that only 10% 1 s are observed). From Lemma 2, α = 0.95 is optimal. We fix k = 10, and test our algorithms with different sizes n. ... we solve the non-convex form with k = 50 for ca-Gr Qc, ca-Hep Ph and k = 100 for Live Journal and My Space. The α and λ values are chosen by a validation set. |