Inductive Global and Local Manifold Approximation and Projection
Authors: Jungeum Kim, Xiao Wang
TMLR 2024 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We have successfully applied both GLo MAP and i GLo MAP to the simulated and real-data settings, with competitive experiments against the state-of-the-art methods. |
| Researcher Affiliation | Academia | Jungeum Kim EMAIL Booth School of Business University of Chicago Xiao Wang EMAIL Statistics Department Purdue University |
| Pseudocode | Yes | Algorithm 1 Global distance construction Algorithm 2 GLo MAP (Transductive dimensional reduction) Algorithm 3 i GLo MAP (Inductive dimensional reduction) |
| Open Source Code | Yes | All implementations are available at https://github.com/Jungeum Kim/i GLo MAP. |
| Open Datasets | Yes | The MNIST database contains 70, 000 28 28 grey images with the class (label) information, and is available at http://yann.lecun.com/exdb/mnist/. |
| Dataset Splits | Yes | we use 60, 000 images for training (and in the later section, the other 10, 000 images for generalization). |
| Hardware Specification | No | The paper does not explicitly describe the hardware used to run its experiments. It mentions computational time comparisons but does not specify the hardware on which these were conducted. |
| Software Dependencies | No | The paper mentions the use of the 'scikit-learn (Pedregosa et al., 2011)' and 'Scikit-learn Python package Buitinck et al. (2013)' but does not provide specific version numbers for these or other key software components like deep learning frameworks (e.g., PyTorch, TensorFlow) or Python itself. |
| Experiment Setup | Yes | We fix all λe to 1 (default), while τ is scheduled to decrease from 1 to 0.1 (default). All other learning hyperparameters are set to their defaults in the i GLo MAP package (learning rate decay = 0.98, Adam s initial learning rate = 0.01, initial particle learning rate = 1, number of neighbors K=15, and mini-batch size=100). GLo MAP was optimized for 300 epochs (500 epochs for MNIST), and i GLo MAP was trained for 150 epochs. |