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[ascl:1711.012] megaman: Manifold Learning for Millions of Points

megaman is a scalable manifold learning package implemented in python. It has a front-end API designed to be familiar to scikit-learn but harnesses the C++ Fast Library for Approximate Nearest Neighbors (FLANN) and the Sparse Symmetric Positive Definite (SSPD) solver Locally Optimal Block Precodition Gradient (LOBPCG) method to scale manifold learning algorithms to large data sets. It is designed for researchers and as such caches intermediary steps and indices to allow for fast re-computation with new parameters.

Code site:
http://mmp2.github.io/megaman/ https://github.com/mmp2/megaman/
Appears in:
http://jmlr.org/papers/v17/16-109.html http://adsabs.harvard.edu/abs/2016arXiv160302763M
Bibcode:
2017ascl.soft11012M
Preferred citation method:

http://jmlr.org/papers/v17/16-109.html


ascl:1711.012
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