Multimodal Linear Discriminant Analysis via Structural Sparsity
Authors: Yu Zhang, Yuan Jiang
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on both synthetic and real-world datasets demonstrate the effectiveness of the proposed MLDA method. |
| Researcher Affiliation | Academia | 1Department of Computer Science and Engineering, Hong Kong University of Science and Technology 2National Key Laboratory for Novel Software Technology, Nanjing University |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any information about the availability of open-source code for the described methodology. |
| Open Datasets | Yes | Six real-world datasets are used in our experiments, including the ETH-80, COIL-20, COIL-100, AR, UMIST, and MNIST databases. |
| Dataset Splits | No | The paper mentions 10-fold cross-validation for parameter selection and that a percentage of data is used for training and the rest for testing, but it does not explicitly define a distinct 'validation' dataset split for general model evaluation separate from the training and test sets. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments (e.g., GPU/CPU models, memory). |
| Software Dependencies | No | The paper mentions algorithms like GIST and proximal average method, but it does not list any specific software dependencies with version numbers. |
| Experiment Setup | Yes | The parameters (i.e., η, σ, and t0) in the GIST algorithm are set to be 2, 0.2 and 1 respectively. The regularization parameters α and β are selected via the 10-fold cross validation method from the candidate set {0.001, 0.01, 0.1, 1} |