TSBP: Tangent Space Belief Propagation for Manifold Learning

Thomas Cohn, Odest Chadwicke Jenkins, Karthik Desingh, Zhen Zeng

We present Tangent Space Belief Propagation (TSBP) as a method for graph denoising to improve the robustness of manifold learning algorithms. Dimension reduction by manifold learning relies heavily on the accurate selection of nearest neighbors, which has proven an open problem for sparse and noisy datasets. TSBP uses global nonparametric belief propagation to accurately estimate the tangent spaces of the underlying manifold at each data point. Edges of the neighborhood graph that deviate from the tangent spaces are then removed. The resulting denoised graph can then be embedded into a lower-dimensional space using methods from existing manifold learning algorithms. Artificially generated manifold data, simulated sensor data from a mobile robot, and high dimensional tactile sensory data are used to demonstrate the efficacy of our TSBP method.

Read the Paper
Explore the Codebase

This paper was published in Robotics and Automation: Letters (RA-L), and was presented at the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Included below is the presentation recording (pre-recorded and presented virtually due to the COVID-19 pandemic), and another video produced for the paper.


  author={Cohn, Thomas and Jenkins, Odest Chadwicke and Desingh, Karthik and Zeng, Zhen},
  journal={IEEE Robotics and Automation Letters},
  title={{TSBP}: Tangent Space Belief Propagation for Manifold Learning},