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