Evidence map›Paper›PMID 37159759›Full record

ArticleProceedings of machine learning research2022

The Manifold Scattering Transform for High-Dimensional Point Cloud Data.

Joyce Chew, Holly R Steach, Siddharth Viswanath, Hau-Tieng Wu, Matthew Hirn, Deanna Needell, Smita Krishnaswamy, Michael Perlmutter

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Article in Proceedings of machine learning research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Learnable Filters for Geometric Scattering Modules.IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society · 2024
    Article
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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Joyce ChewUCLA Department of Mathematics, Los Angeles, CA, USA.
Holly R SteachYale University, Department of Genetics, New Haven, CT, USA.
Siddharth ViswanathUC Irvine Department of Computer Science, Irvine, CA, USA.
Hau-Tieng WuDuke University, Department of Mathematics, Durham, NC, USA.
Matthew HirnMichigan State University, Department of Mathematics, East Lansing, USA.
Deanna NeedellUCLA Department of Mathematics, Los Angeles, CA, USA.
Smita KrishnaswamyYale University, Department of Genetics, New Haven, CT, USA.
Michael PerlmutterUCLA Department of Mathematics, Los Angeles, CA, USA.

Funding

Human and Translational Immunology Training ProgramT32AI155387 · NIAID · YALE UNIVERSITY · PI Joseph Edgar Craft, Kevan C Herold · 2021 to 2026
$2.6M
Finding emergent structure in multi-sample biological data with the dual geometry of cells and features R01GM135929 · NIGMS · MICHIGAN STATE UNIVERSITY · PI BROWN, EDWARD · 2019 to 2022
$1.4M
NIAID NIH HHS T32 AI155387NIGMS NIH HHS R01 GM135929
6 · The paper itself

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.

Identifiers

PMID37159759
PMCPMC10164360

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.