We introduce multi-frequency vector diffusion maps (MFVDM), a new framework for organizing and analyzing high dimensional data sets. The new method is a mathematical and algorithmic generalization of vector diffusion maps (VDM) and other non-linear dimensionality reduction methods. The idea of MFVDM is to incorporates multiple unitary irreducible representations of the alignment group which introduces robustness to noise. We illustrate the efficacy of MFVDM on synthetic and cryo-EM image datasets, achieving better nearest neighbors search and alignment estimation than other baselines as VDM and diffusion maps (DM), especially on extremely noisy data.
Yifeng Fan (University of Illinois at Urbana-Champaign)
Jane Zhao (University of Illinois at Urbana Champaign)
Related Events (a corresponding poster, oral, or spotlight)
2019 Oral: Multi-Frequency Vector Diffusion Maps »
Thu Jun 13th 12:00 -- 12:05 AM Room Room 201