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Paper detail

Successive Subspace Learning: An Overview

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.

preprint2021arXivOpen access
Mozhdeh RouhsedaghatMasoud MonajatipoorZohreh AziziC. -C. Jay Kuo
Computer Vision
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Mozhdeh RouhsedaghatMasoud MonajatipoorZohreh AziziC. -C. Jay Kuo

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