THE MANIFOLD SCATTERING TRANSFORM FOR HIGH-DIMENSIONAL POINT CLOUD DATA

Joyce Chew, Holly Steach, Siddharth Viswanath, Hau Tieng Wu, Matthew Hirn, Deanna Needell, Matthew D. Vesely, Smita Krishnaswamy, Michael Perlmutter

Research output: Contribution to journalConference articlepeer-review

Abstract

The manifold scattering transform is a deep feature extractor for data defined on a Riemannian manifold. It is one of the first examples of extending convolutional neural network-like operators to general manifolds. The initial work on this model focused primarily on its theoretical stability and invariance properties but did not provide methods for its numerical implementation except in the case of two-dimensional surfaces with predefined meshes. In this work, we present practical schemes, based on the theory of diffusion maps, for implementing the manifold scattering transform to datasets arising in naturalistic systems, such as single cell genetics, where the data is a high-dimensional point cloud modeled as lying on a low-dimensional manifold. We show that our methods are effective for signal classification and manifold classification tasks.

Original languageEnglish (US)
Pages (from-to)67-78
Number of pages12
JournalProceedings of Machine Learning Research
Volume196
StatePublished - 2022
EventICML Workshop on Topology, Algebra, and Geometry in Machine Learning, TAG:ML 2022 - Virtual, Online, United States
Duration: Jul 20 2022 → …

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering
  • Statistics and Probability

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