Topologically Regularized Data Embeddings
Authors: Robin Vandaele, Bo Kang, Jefrey Lijffijt, Tijl De Bie, Yvan Saeys
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We include thorough experiments on synthetic and real data that highlight the usefulness and versatility of this approach, with applications ranging from modeling high-dimensional single-cell data, to graph embedding. |
| Researcher Affiliation | Academia | Robin Vandaele, Bo Kang, Jefrey Lijffijt, Tijl De Bie, Yvan Saeys Ghent University, Gent, Belgium |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available on github.com/robinvndaele/topembedding. |
| Open Datasets | Yes | We considered a single-cell trajectory data set of 264 cells in a 6812-dimensional gene expression space (Cannoodt et al., 2018; Saelens et al., 2019). |
| Dataset Splits | Yes | All points in the 2D embeddings were then split into 90% points for training and 10% for testing. Consecutively, we used 5-fold CV on the training data to tune the regularization hyperparameter C {1e 2, 1e 1, 1, 1e1, 1e2}. |
| Hardware Specification | Yes | Topological optimization was performed in Pytorch on a machine equipped with an Intel Core TM i7 processor at 2.6GHz and 8GB of RAM, using code adapted from Br uel-Gabrielsson et al. (2020). |
| Software Dependencies | No | The paper mentions software like Pytorch, SCIKIT-LEARN, and Dionysus but does not provide specific version numbers for these dependencies. |
| Experiment Setup | Yes | Tables 1 & 2 summarize data sizes, hyperparameters, losses, and optimization times. |