megaman: Manifold Learning for Millions of Points

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Ada Coda
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megaman: Manifold Learning for Millions of Points

Post by Ada Coda » Thu Nov 30, 2017 8:10 pm

megaman: Manifold Learning for Millions of Points

Abstract: 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.

Credit: McQueen, James; Meila, Marina; VanderPlas, Jacob; Zhang, Zhongyue

Site: https://github.com/mmp2/megaman/
http://adsabs.harvard.edu/abs/2016arXiv160302763M

Bibcode: 2017ascl.soft11012M

Preferred citation method: https://jmlr.csail.mit.edu/papers/v17/16-109.html

ID: ascl:1711.012
Last edited by Ada Coda on Sun Aug 09, 2020 9:07 pm, edited 1 time in total.
Reason: Updated code entry.

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