mlsanityData to 13 Oct 2026

Successive Subspace Learning: An Overview

arXiv 2103.00121 ↗First posted 27 Feb 2021cs.CVcited by 11

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Abstract (arXiv)

Successive Subspace Learning (SSL) offers a light-weight unsupervised feature learning method based on inherent statistical properties of data units (e.g. image pixels and points in point cloud sets). It has shown promising results, especially on small datasets. In this paper, we intuitively explain this method, provide an overview of its development, and point out some open questions and challenges for future research.

Key facts. Cited by 11 papers in the map (data through 13 Oct 2026). Builds on 9 of its 11 references that are in the map. First posted on arXiv 27 Feb 2021.

Builds on

9 of 11 references.

References · 9

  1. 2018Interpretable Convolutional Neural Networks via Feedforward Designcited by 49
  2. 2020PixelHop++: A Small Successive-Subspace-Learning-Based (SSL-based) Model for Image Classificationcited by 37
  3. 2016Understanding Convolutional Neural Networks with A Mathematical Modelcited by 33
  4. 2017On Data-Driven Saak Transformcited by 22
  5. 2020PointHop++: A Lightweight Learning Model on Point Sets for 3D Classificationcited by 22
  6. 2021VoxelHop: Successive Subspace Learning for ALS Disease Classification Using Structural MRIcited by 18
  7. 2020FaceHop: A Light-Weight Low-Resolution Face Gender Classification Methodcited by 16
  8. 2020NITES: A Non-Parametric Interpretable Texture Synthesis Methodcited by 13
  9. 2020Low-Resolution Face Recognition In Resource-Constrained Environmentscited by 10

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Cited by year

Citing papers per year: 2021: 6; 2022: 4; 2023: 1.

11 papers.