Riemannian Submanifold Tracking on Low-Rank Algebraic Variety
Authors: Qian Li, Zhichao Wang
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive comparison experiments demonstrate the accuracy and efficiency of RIST algorithm. |
| Researcher Affiliation | Academia | Qian Li Chinese Academy of Sciences Beijing, China qianli.charlene@gmail.comZhichao Wang Tsinghua University Beijing, China wzchary@gmail.com |
| Pseudocode | Yes | Algorithm 1 Rank Initialization; Algorithm 2 RIST: Riemann Submanifold Tracking; Algorithm 3 ROM: Riemann Optimization over Mk |
| Open Source Code | No | The paper does not contain any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | Two collaborative filter datasets: the Jester-all dataset (Goldberg et al. 2001) and Movie-10M dataset (Herlocker et al. 1999) are used for the collaborative filtering. |
| Dataset Splits | No | The paper mentions training and test sets but does not explicitly describe a separate validation split, its size, or how it was used. |
| Hardware Specification | Yes | All comparison algorithms are implemented in Matlab and tested on a desktop computer with a 3.20 GHz CPU and 4.00 GB of memory. |
| Software Dependencies | No | The paper states "All comparison algorithms are implemented in Matlab" but does not specify a version number for Matlab or any other software dependencies. |
| Experiment Setup | Yes | The parameters ρ and η of RIST are set as 1.5 and 0.04, respectively. We set the rank parameter of these comparison methods as the ground-truth, namely, 15, 25 and 35. The parameters η of RIST is 0.05. |