Riemannian Pursuit for Big Matrix Recovery
Authors: Mingkui Tan, Ivor W. Tsang, Li Wang, Bart Vandereycken, Sinno Jialin Pan
ICML 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | experimental results show that it substantially outperforms existing methods when applied to large-scale and ill-conditioned matrices. |
| Researcher Affiliation | Academia | 1 School of Computer Science, The University of Adelaide, Ingkarni Wardli North Terrace Campus 5005, Australia 2 Center for Quantum Computation & Intelligent Systems, University of Technology Sydney, Australia 3 Department of Mathematics, University of California, San Diego, USA 4 Department of Mathematics, Princeton University, Fine Hall, Washington Road, Princeton NJ 08544-1000, USA 5 Institute for Infocomm Research, 1 Fusionopolis Way, #21-01 Connexis (South) 138632, Singapore |
| Pseudocode | Yes | Algorithm 1 RP: Riemannian Pursuit for MR. and Algorithm 2 NRCG(Xintial,r, ϵin) for solving (2). |
| Open Source Code | No | The paper does not provide an explicit statement or link to the source code for the proposed Riemannian Pursuit (RP) method. |
| Open Datasets | Yes | Movie Lens with 10M ratings (denoted by Movie-10M) (Herlocker et al., 1999) and Netflix Prize dataset (KDDCup, 2007). |
| Dataset Splits | Yes | we report the testing RMSE of different methods over 10 random 80/20 train/test partitions as explained in (Laue, 2012). |
| Hardware Specification | Yes | All the experiments (except for GECO) are conducted in Matlab on a PC installed a 64-bit operating system with an Intel(R) Core(TM) i7 CPU (2.80GHz with single-thread mode) and 24GB memory. |
| Software Dependencies | No | The paper states that experiments were conducted 'in Matlab' but does not provide a specific version number for Matlab or any other software dependencies with version numbers. |
| Experiment Setup | Yes | We set λF = 0.01, ϵout = 10 5 for the stopping conditions and η = 0.65 for RP. For APG, we set the trade-off parameter λ = 10 3σmax, where σmax is the largest singular value of A (b). For all the fixed-rank methods, we set r = br = 50. |