NOTES: ---------------------------------- * SETUP: ---------------------------------- + run setup for setpath for figtree (precompiled for Mac and Linux) ---------------------------------- * DEMO: ---------------------------------- + test_dFIM: randomly generate two persistence diagrams, and compute the Fisher information metric between them, with or without Fast Gauss Transform. ---------------------------------- * FUNCTIONS in LIB: ---------------------------------- + compute_dFIM_distance: compute Fisher information metric between two persistence diagrams (without Fast Gauss Transform --- Quadratic complexity) <Algorithm 1 in the NIPS'18 paper) + compute_dFIM_distance_FGT: compute Fisher information metric between two persistence diagrams, approximated by Fast Gauss Transform --- Linear complexity <Algorithm 1 in the NIPS'18 paper> + Third party toolbox (figtree-0.9.3): Fast Gauss Transform library (Link: http://www.umiacs.umd.edu/~morariu/figtree/ or http://sourceforge.net/projects/figtree) ---------------------------------- RELEVANT PAPER: ---------------------------------- Tam Le, Makoto Yamada, Persistence Fisher Kernel: A Riemannian Manifold Kernel for Persistence Diagrams, Neural Information Processing Systems (NIPS), Canada, 2018. ArXiv link: https://arxiv.org/abs/1802.03569 ---------------------------------- * CONTACT ---------------------------------- % Version 0.1 (October 19th, 2018) @ Tam Le - RIKEN AIP Email: tam.le@riken.jp Homepage: https://sites.google.com/site/lttamvn/ Please contact me if you observe any bugs in the execution of the algorithms. Many thanks !!!
lttam/PersistenceFisher
Compute Persistence Fisher distance (Fisher information metric between two persistence diagrams with and without Fast Gauss Transform) --- Algorithm 1 in Tam Le & Makoto Yamada NIPS'18
MATLABGPL-2.0