SpaceMAP: Visualizing High-Dimensional Data by Space Expansion
Authors: Xinrui Zu, Qian Tao
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluated Space MAP on a range of synthetic and real datasets with varying manifold properties, and demonstrated its excellent performance in comparison with classical and state-of-the-art DR methods. In particular, the concept of space expansion provides a generic framework for understanding nonlinear DR methods including the popular t-distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP). |
| Researcher Affiliation | Academia | Xinrui Zu 1 Qian Tao 1 ... 1Department of Imaging Physics, Delft University of Technology. Correspondence to: Xinrui Zu <X.Zu1@tudelft.nl>, Qian Tao <Q.Tao@tudelft.nl>. |
| Pseudocode | Yes | Algorithm 1 describes the maximum likelihood estimation (MLE) of the intrinsic dimensions. |
| Open Source Code | No | The paper does not contain an explicit statement about releasing the source code for the Space MAP methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | Experiments were performed on a wide range of datasets, including the standard MNIST (Le Cun, 1998), Fashion-MNIST (Xiao et al., 2017), Swiss roll 1 (on the surface we dig a hole to test if visualization methods can preserve the local property on a continuous manifold), Swiss roll 2 (consisting of parallel lines to test the hierarchical manifold assumption), COIL-20 (Nene et al., 1996), RNA-seq (Tasic et al., 2018). |
| Dataset Splits | Yes | For quantitative evaluation, we computed the 20-fold crossvalidated KNN classification accuracy, trustworthiness, continuity, Shepard goodness, and normalized stress to evaluate both local and global structure preservation (Espadoto et al., 2021; Nonato & Aupetit, 2019). |
| Hardware Specification | Yes | We implemented all the DR methods on a Ubuntu 20.04 LTS workstation platform with AMD 3900x 4.2GHz 12-core CPU, 64GB DDR4 RAM and NVidia RTX 3090 24GB GPU. |
| Software Dependencies | No | The paper mentions using 'Scikit-learn Barnes-Hut t-SNE implementation' and 'umap-learn implementation' but does not specify their version numbers or any other software dependencies with version details. |
| Experiment Setup | Yes | Two numbers are empirically set: knear = 20, and kmiddle = 50. ... For t-SNE, we use Scikit-learn Barnes-Hut t-SNE implementation with PCA initialization and the default perplexity is 30. For UMAP, we use the original umap-learn implementation with spectral embedding initialization, the default number of neighbors for each point is n-neighbors=15. |