Work

Successive Subspace Learning: An Overview

Year: 2021

Type: preprint

Abstract: 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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2103.00121

Authors: Rouhsedaghat, Mozhdeh, Monajatipoor, Masoud, Azizi, Zohreh, Kuo, C. -C. Jay

Institutions: University of Southern California, University of California, Los Angeles

Language: English

Topic: Face and Expression Recognition

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W3135584811

Cites: 11

Cited by: 13

Related to: 20