NE: Neighbor Embedding for visualization
NE: Neighbor Embedding for visualization [Download]
This software package implements scalable optimization of Neighbor Embedding (NE), a set of a dimensionality reduction algorithms recently developed for data visualization. An NE finds a 2D or 3D mapping of high-dimensional data, trying to preserve the neighborhood according to certain information divergences. Typical NEs include
- Stochastic Neighbor Embedding (SNE)
- Symmetric Stochastic Neighbor Embedding (s-SNE)
- Student t-Distributed Stochastic Neighbor Embedding (t-SNE)
- Elastic Embedding (EE)
- Neighbor Retrieval Visualizer (NeRV)
- Weighted t-SNE (wt-SNE)
On the right is the scatter plot of the MNIST handwritten digits. See below or in the gallery for more visualization examples by using NE.
The usage is decribed in "readme.txt" in the package.
If you use the NE software in any publishable work, please cite at least one of the following papers:
- Zhirong Yang, Jaakko Peltonen and Samuel Kaski. Scalable Optimization
of Neighbor Embedding for Visualization.
In International Conference on
Machine Learning (ICML2013), pages 127-135,
Atlanta, USA, 2013.
[pdf]
[supplemental]
- Zhirong Yang, Jaakko Peltonen and Samuel Kaski. Optimization Equivalence of Divergences Improves Neighbor Embedding.
In International Conference on
Machine Learning (ICML2014), pages 460-468, Beijing, China 2014.
[pdf]
[supplemental]
This is experimental software provided as is; we welcome any comments and corrections but cannot give any guarantees about the code. If you have any comments or bug reports, please direct them to Zhirong Yang.