SpaceMAP: Visualizing High-dimensional data by Space Expansion
Project description
SpaceMAP
SpaceMAP is a dimensionality reduction method utilizing the local and global intrinsic dimensions of the data to better alleviate the 'crowding problem' analytically.
Paper
https://icml.cc/virtual/2022/spotlight/18170
https://proceedings.mlr.press/v162/zu22a.html
Hyper-parameters
SpaceMAP has 4 main hyper-parameters: n-near/n-middle and d-local/d-global, which define the intrinsic dimensions and the hierarchical manifold approximation.
- n-near: number of neighbors in the near fields of each data point. (default: 20)
- n-middle: number of neighbors in the middle field of each data point. (default: 1% of the whole dataset)
- d-local: estimated intrinsic dimensions of the near fields of each data point. (default: Auto)
- d-global: estimated intrinsic dimension of the whole dataset. (default: Auto)
Installation
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