Big Learning Expectation Maximization
Authors: Yulai Cong, Sijia Li
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through simulated experiments, we empirically show that the Big Learn-EM is capable of delivering the optimal with high probability; comparisons on benchmark clustering datasets further demonstrate its effectiveness and advantages over existing techniques. |
| Researcher Affiliation | Academia | Yulai Cong*, Sijia Li Sun Yat-sen University yulaicong@gmail.com, lisijia57@163.com |
| Pseudocode | Yes | Algorithm 1: Big Learning Expectation Maximization |
| Open Source Code | Yes | The code is available at https://github.com/Yulai Cong/Big-Learning Expectation-Maximization. |
| Open Datasets | Yes | To validate the effectiveness of the Big Learn-EM in real-world clustering applications, we conduct comprehensive experiments on diverse clustering datasets, including Connect-4, Covtype, Glass, Letter, Pendigits, Satimage, Seismic, Svmguide2, and Vehicle (see Appendix B for details). We follow the experimental setup in Cai et al. (2022) and conduct an experiment on the Fashion MNIST dataset. |
| Dataset Splits | No | The paper describes the use of 'test joint KL divergence' but does not specify the explicit percentages or sample counts for training, validation, and test splits needed for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'scipy.stats.ortho group' in Algorithm 1, implying Python and SciPy usage, but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | Algorithm 1: Input: Training data, the number K of mixture components, probabilities [P1, P2] for joint and marginal matchings, and the number W of local updates. The training objective in (12) also mentions a hyper-parameter γ and η > 0 is a small constant (11). |