Self-Paced Learning for Matrix Factorization
Authors: Qian Zhao, Deyu Meng, Lu Jiang, Qi Xie, Zongben Xu, Alexander Hauptmann
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the performance of the proposed SPMF approach...on synthetic, structure from motion and background subtraction data. |
| Researcher Affiliation | Academia | 1School of Mathematics and Statistics, Xi an Jiaotong University 2School of Computer Science, Carnegie Mellon University |
| Pseudocode | Yes | Algorithm 1 Self-paced matrix factorization algorithm |
| Open Source Code | No | The paper mentions using 'publicly available codes from the authors websites' for competing methods, but does not state that its own code is publicly available or provide a link. |
| Open Datasets | Yes | For rigid SFM, we employ the Dinosaur sequence1 which contains 319 feature points tracked over 36 views... 1http://www.robots.ox.ac.uk/ abm/. For nonrigid SFM, we use the Giraffe sequence2, which includes 166 feature points tracked over 120 frames. 2http://www.robots.ox.ac.uk/ abm/. Four video sequences provided by Li et al. (2004)4 were adopted in our evaluation... 4http://perception.i2r.a-star.edu.sg/bkmodel/bkindex |
| Dataset Splits | No | The paper describes data generation and corruption (e.g., missing data, noise addition) and reports performance averaged over multiple runs or realizations, but it does not specify explicit training/validation/test dataset splits with percentages or counts for reproducibility. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running experiments. |
| Software Dependencies | No | The paper refers to modifying existing solvers, but does not provide specific software names with version numbers for any libraries or dependencies used in their implementation. |
| Experiment Setup | Yes | Input: Incomplete data matrix Y Rm n with observation indexed by Ω, k0, kend, µ > 1. where parameter γ > 0 is introduced to control the strength of the weights assigned to the selected samples. the rank was set to 4 and 6 for rigid and nonrigid SFM, respectively. |