Dear friends, I’m sharing this great news that Google opens Embedding Projector for the world. Now everyone could enjoy the convenient approach to visualize high dimensional data on the web browser.
Based on my personal programming experience, t-SNE is a great method for high-dimensional data visualization, better than PCA on some data sets like MNIST.
You can download the Matlab Toolbox for Dimensionality Reduction; or download the t-SNE method for different platforms: t-SNE codes.
Moreover, check the following papers if you want to learn more details about dimensionality reduction.
- L.J.P. van der Maaten and G.E. Hinton. Visualizing High-Dimensional Data Using t-SNE. Journal of Machine Learning Research 9(Nov):2579-2605, 2008. PDF [Supplemental material] [Talk]
- L.J.P. van der Maaten, E.O. Postma, and H.J. van den Herik. Dimensionality Reduction: A Comparative Review. Tilburg University Technical Report, TiCC-TR 2009-005, 2009. PDF
Here are the links for more information:
- The Original Blog of Google Research: https://research.googleblog.com/2016/12/open-sourcing-embedding-projector-tool.htm
l - Embedding Projector: http://projector.tensorflow.org/ (Note: when running MNIST, it may slow down your computer, be careful to this).
- Related Technique of t-SNE: http://distill.pub/2016/misread-tsne/
More about the Authors.
Laurens van der 
Maaten
Github: https://lvdmaaten.github.io/
He is now a Research Scientist at Facebook AI Research in New York, working on machine learning and computer vision.
Geoffrey Hinton
Geoffrey Hinton’s website: http://www.cs.toronto.edu/~hinton/
Hinton works part-time for Google as an Engineering Fellow and part-time for the University of Toronto as an Emeritus Distinguished Professor.
He is one Godfather of Deep Learning among others, such as Yann LeCun, Yoshua Bengio etc.
Coursera : Neural Networks for Machine Learning
C. Cui's Blog 