Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Multimodal Linear Discriminant Analysis via Structural Sparsity
Authors: Yu Zhang, Yuan Jiang
IJCAI 2017 | Venue PDF | 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} |