Matrix Variate Gaussian Mixture Distribution Steered Robust Metric Learning
Authors: Lei Luo, Heng Huang
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive evaluations on the real-world data. Experimental results show our method consistently outperforms the related state-of-the-art methods. |
| Researcher Affiliation | Academia | Lei Luo, Heng Huang Electrical and Computer Engineering, University of Pittsburgh, USA lel94@pitt.edu, heng.huang@pitt.edu |
| Pseudocode | Yes | Algorithm 1 Solving Model (8) via EM |
| 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 | In this section, five standard databases, including FGNET Aging database (Lanitis, Taylor, and Cootes 2002), Visible (VI) and Near-Infrared (NIR) Face Database (Shen et al. 2011), OSR database (Parikh and Grauman 2011), Pub Fig database (Kumar et al. 2009) and LFW database (Huang et al. 2007), are selected to evaluate the effectiveness of our method. |
| Dataset Splits | No | The paper consistently mentions "training samples" and "test samples" but does not explicitly describe a separate "validation" dataset split used for hyperparameter tuning or early stopping, which is typically part of a full train/validation/test split for reproducibility. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU models, CPU types) used to run the experiments. |
| Software Dependencies | No | The paper mentions other software used for comparison (e.g., SVM (Fan et al. 2008), Liblinear) but does not provide specific version numbers for any software dependencies required to replicate their own method's experiments. |
| Experiment Setup | Yes | For our method, we set λ = 0.001, ξ = 10^-5 and R = 5. For other compared methods, we follow the authors suggestions to choose the optimal parameters. |