Probabilistic Rank-One Matrix Analysis with Concurrent Regularization
Authors: Yang Zhou, Haiping Lu
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on both synthetic and real-world data demonstrate the superiority of PROMA in subspace estimation and classification as well as the effectiveness of concurrent regularization in regularizing bilinear PPCAs. |
| Researcher Affiliation | Academia | Yang Zhou and Haiping Lu Hong Kong Baptist University, Hong Kong, China yangzhou@comp.hkbu.edu.hk, haiping@hkbu.edu.hk |
| Pseudocode | Yes | Algorithm 1 Probabilistic Rank-One Matrix Analysis |
| Open Source Code | No | The paper does not provide any specific links or statements regarding the release of its own source code for the described methodology. It only mentions thanking 'Prof. Jianhua Zhao for sharing their codes', which refers to third-party code. |
| Open Datasets | Yes | Two face data sets are tested. The first one is a subset of the FERET database [Phillips et al., 2000]... The second one is a subset from the PIE database [Sim et al., 2003]... |
| Dataset Splits | No | The paper states, 'We randomly split the FERET and PIE data sets into training and test sets so that each subject has L images for training, and the rest are used for test,' but does not explicitly mention a validation split. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependency details with version numbers required to replicate the experiment. |
| Experiment Setup | Yes | For MPCA, TROD, and UMPCA, we use their default settings with up to 1, 10, and 10 iterations, respectively. BPPCA has both MLE and MAP implementations. We choose the MLE-based one used in face recognition [Zhao et al., 2012] by iterating until convergence. We iterate PROMANR, PROMASR, and PROMA until convergence or 500 iterations. The regularization parameter γ of PROMA is automatically determined by PROMANR with P = 1. |