Navigating the Effect of Parametrization for Dimensionality Reduction
Authors: Haiyang Huang, Yingfan Wang, Cynthia Rudin
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experiments Here, we evaluate the performance of our Param Pa CMAP and Param Respulsor algorithms empirically. To contextualize our findings, we juxtapose our results against those obtained from other contemporary parametric DR algorithms. Visualization for the embeddings generated by all algorithms can be found in App. C. |
| Researcher Affiliation | Academia | Haiyang Huang Yingfan Wang Cynthia Rudin Duke University {hyhuang, yw416, cynthia}@cs.duke.edu |
| Pseudocode | Yes | Pseudocode for Param Repulsor is found in Alg. 1 and detailed in Alg. 2 in App. F. |
| Open Source Code | Yes | Our code is available at https://github.com/hyhuang00/Param Repulsor. |
| Open Datasets | Yes | For image analysis, we analyzed the MNIST [15] and Fashion-MNIST (F-MNIST) [32] datasets, along with COIL-20 [33] and COIL-100 [34]. |
| Dataset Splits | Yes | We perform leave-one-out cross validation, and utilize a k-NN classifier to predict the label of the point. |
| Hardware Specification | Yes | All experiments are conducted with an Exxact Tensor EX 2U Server with 2 Intel Xeon Ice Lake Gold 5317 Processors @ 3.0GHz. We limit the RAM usage to be 32GB. Parallel computation are performed over a single Nvidia RTX A5000 GPU. |
| Software Dependencies | Yes | Param Repulsor and Param Pa CMAP are implemented with Py Torch 2.0.0, Numba 0.57.0 and CUDA 11.7. |
| Experiment Setup | Yes | Unless otherwise specified, we utilize a network of three hidden layers with [100, 100, 100] neurons. Param Repulsor utilizes Si LU as the activation function, whereas Param Pa CMAP utilizes Re LU just as the other methods. |